{"as_of":"2026-08-11T06:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:928df8d22aa0b8cb8cd07e419e9de2688068fd359240960bcc21deac47d7f881","coverage":[{"denominator":92,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":92,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T10:28:41.613505Z","state":"measured"},{"denominator":92,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":92,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.07316/citation-record","integrity":"/paper/2608.07316/integrity","json":"/paper/2608.07316/citation-record.json","paper":"/paper/2608.07316"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:39.983288Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:39.983288Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:885f79d56e1e0b580d351f7c16f1149775ea0b6cce9da12e151445934ca9e8f9","observation_id":"b1e2655d-4c2b-46e5-bd06-44415487eec4","resolution":{"observed_at":"2026-08-10T10:28:39.983288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:39.987594Z","title":"Self-efficacy and locus of control when facing listening challenges: Validation of the listening challenges attitude scale (licas)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:39.987594Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:a3f447e1dfc2f97fdc5197d22537d9d200072eb1670787c2108585b69f6eab8f","observation_id":"745c874e-c794-42d5-9f15-e4479ad66d4c","resolution":{"observed_at":"2026-08-10T10:28:39.987594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:39.991313Z","title":"Exploratory factor analysis: Current use, methodological developments and recommendations for good practice.Current psychology, 40(7):3510–3521, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:39.991313Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:95bd3929f03c2cec328415b963a7527dd74e5ef5e4b16d2c807b4e59c7255e0f","observation_id":"de169a24-1582-4adc-baa0-bd508c75b24d","resolution":{"observed_at":"2026-08-10T10:28:39.991313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:39.995346Z","title":"Dasentimental: Detecting depression, anxiety, and stress in texts via emotional recall, cognitive networks, and machine learning.Big data and cognitive computing, 5(4):77, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:39.995346Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:bd4ec1214b0f551ddc316fe49845368fd5a419f75c0ef9478d0b93102eaba2c5","observation_id":"46b8bff0-72b5-49dc-9e1a-5ff7c4d4f916","resolution":{"observed_at":"2026-08-10T10:28:39.995346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.16935","last_updated":"2026-04-18T09:39:22Z","snapshot_observed_at":"2026-07-06T23:04:10.324443Z","submitted_at":"2026-04-18T09:39:22Z","title":"LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.16935","snapshot_observed_at":"2026-08-10T10:28:39.999382Z","title":"Llms can persuade only psychologically susceptible humans on societal issues, via trust in ai and emotional appeals, amid logical fallacies.arXiv preprint arXiv:2604.16935, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:39.999382Z"},"links":{"cited_paper":"/paper/2604.16935","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:b7e9f1a7c4e1567b702a5feac799057de8af5892cb74e0552a96dcd90db0daf1","observation_id":"d5ac2b1e-3725-4979-8c95-318987b7e3d5","resolution":{"observed_at":"2026-08-10T10:28:39.999382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.003838Z","title":"Examining linguistic differences in electronic health records for diverse patients with diabetes: natural language processing analysis.JMIR Medical Informatics, 12(1):e50428, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.003838Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:f97eee19868b7dd6538478976d77e3f9024dc022da66dddb0addf1bbbbec2b8d","observation_id":"077a0456-a707-4b91-a3ba-48a4971e7801","resolution":{"observed_at":"2026-08-10T10:28:40.003838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.007891Z","title":"Language is primarily a tool for communication rather than thought.Nature, 630(8017):575–586, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.007891Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:38c9f05124e0056af7efe26d7a7d0138a27f64e73cdda9d24116d13d4cb1e5d9","observation_id":"89e3c163-096c-46be-bd19-332e82ee5719","resolution":{"observed_at":"2026-08-10T10:28:40.007891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.052575Z","title":"John Wiley & Sons, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.052575Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:36cb7ce405df57c67da8b6fec7e7e069b6917cc7b89f94a14055a71d0318a703","observation_id":"16f94765-ac0a-4a1e-8ca1-0cef0e55e18f","resolution":{"observed_at":"2026-08-10T10:28:40.052575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.110876Z","title":"Deep lexical hypothesis: Identifying personality structure in natural language.Journal of Personality and Social Psychology, 125(1):173, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.110876Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:38e01eec195e11b47d2111ef99a15c373ccec547f71349e2da274364cad77c56","observation_id":"88ddbb29-0316-48b9-a1bf-b5286802f983","resolution":{"observed_at":"2026-08-10T10:28:40.110876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.144611Z","title":"Emoatlas: An emotional network analyzer of texts that merges psychological lexicons, artificial intelligence, and network science.Behavior Research Methods, 57(2):77, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.144611Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:3ddd3b8bbde7af177ab9ba88a6e089441c15e989e5487abb90ddaec6d1e43270","observation_id":"ded9a208-4df7-4188-a4b7-2f97ac9102f6","resolution":{"observed_at":"2026-08-10T10:28:40.144611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.245992Z","title":"Using complex networks to understand the mental lexicon","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.245992Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:0e159a03bb63788bb7375fe35c8e5ec72f9c5e47dbe7d111ce4c3c38184d0ca3","observation_id":"6613b261-354f-4214-9801-affb04556e83","resolution":{"observed_at":"2026-08-10T10:28:40.245992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.301944Z","title":"Structure and flexibility: Inves- tigating the relation between the structure of the mental lexicon, fluid intelligence, and creative achievement","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.301944Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:df48daf4c2452679b9fcb36a9ce33654ee1a83f8b5cc0975258d8d019e4def32","observation_id":"f3b2f4fe-67ea-4414-b797-ab2dcf1f0ca5","resolution":{"observed_at":"2026-08-10T10:28:40.301944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.349388Z","title":"Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.Psychonomic Bulletin & Review, 31(5):1981–2004, 2024","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.349388Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:8c8142c50ea3fd4ededca9ae1b16b989512f8afa1dde7fbf7b29829c0fed6927","observation_id":"19aeb13c-ecf8-4c5b-943a-e7522a81f37e","resolution":{"observed_at":"2026-08-10T10:28:40.349388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.359175Z","title":"In an absolute state: Elevated use of absolutist words is a marker specific to anxiety, depression, and suicidal ideation.Clinical psychological science, 6(4):529–542, 2018","venue":null,"work_id":"ce51a0c9-9ab4-49f7-af0c-ebe1fd879392","year":2018},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.501402Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:078aee5c8897a53bd88fa0a089840d85b861c933fd240823b075842493ea76f4","observation_id":"6c9ee9ee-f456-49a3-b505-e07068253408","resolution":{"observed_at":"2026-08-10T10:28:44.363328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.347011Z","title":"How do users of a mental health app conceptualise digital therapeutic alliance? a qualitative study using the framework approach","venue":null,"work_id":"911993c2-c92d-4906-b0bd-1d32c92e9c70","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.638928Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:cfd9bb3798484b9ba69c398279fe757fee7608e6b498eb9b391a08215da5df0a","observation_id":"6e979e0e-1a35-40db-806b-850b9eab5b87","resolution":{"observed_at":"2026-08-10T10:28:44.351986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.335508Z","title":"Detecting and measuring depression on social media using a machine learning approach: systematic review.JMIR Mental Health, 9(3): e27244, 2022","venue":null,"work_id":"2c2db6a6-18d7-4bee-a86e-0c3217934ac7","year":2022},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.643087Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:c4099cc4cfdd3167586ed128d3e0b7c3eba35fd5760489fbfd8c38628474eb1d","observation_id":"411fa2f5-6def-4ee7-964d-87681d7c24c9","resolution":{"observed_at":"2026-08-10T10:28:44.338797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.324728Z","title":"Predicting depression via social media","venue":null,"work_id":"8db7ed63-d15e-43ce-8a0c-f0f2bce4a666","year":2013},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.646966Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:8eee7195aa8675d7a133290ad42fd089e774c62fa42a5038ad92c4a068cc6f4b","observation_id":"3bb1bbf1-2acd-4bc5-9541-05907a09b50c","resolution":{"observed_at":"2026-08-10T10:28:44.328008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.313674Z","title":"Digital shadows in mental health map how llms simulate depression, anxiety, and stress through language and psychometrics.PsyArXiv,","venue":null,"work_id":"dea0c803-36e8-49d0-84b3-5896b103bf47","year":null},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.650794Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:d542b91bfba8d10cb418c6ca885a201356d836e8e0a36572966f1b6efb96cb5e","observation_id":"b056d8d3-b271-4a7f-a471-e031b8fbf63c","resolution":{"observed_at":"2026-08-10T10:28:44.317609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.256793Z","title":"Harvard University, 2024","venue":null,"work_id":"6372d06b-1488-433e-b0cb-0b81d170fb9d","year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.658792Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:c3c02cd56ccb383ce6e7d29f04497b03563a05efa16b3008bb749de6bb017f81","observation_id":"2431d8f9-d7b7-4d8c-bb6f-dd5623e4459e","resolution":{"observed_at":"2026-08-10T10:28:44.306564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:44.033933Z","title":"Text psychometrics: Assessing psychological constructs in text using natural language processing, 2026","venue":null,"work_id":"cde77b8f-e469-4ad4-ae10-161eff9fc468","year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.662717Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:04bdbd3456e574cc192db456b7b983e855beb0d7cd556163f7ef789575fffdf8","observation_id":"39780863-611b-48a5-8fb3-1cfaa77e8dd1","resolution":{"observed_at":"2026-08-10T10:28:44.138565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.942784Z","title":null,"venue":null,"work_id":"995b6cf5-8309-471d-9f2e-6333ec77b9dc","year":1995},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.666306Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:8330b02028d978a428f0a12a41e02e8a31081849ac9027cad58a72b031836cc1","observation_id":"f81dee48-e414-4b99-a789-66eada5bae9e","resolution":{"observed_at":"2026-08-10T10:28:43.947161Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.931888Z","title":"The phq-9: validity of a brief depression severity measure.Journal of general internal medicine, 16(9):606–613, 2001","venue":null,"work_id":"1d451f81-7e36-402b-aa4c-29d9ece0bd58","year":2001},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.669938Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:8a1d80fdde85393db8c5d72eb45d9c68c687ff48d65ac5cc6c086dd6d5f98fb0","observation_id":"7c6fa032-e694-41e5-abe3-5d1b4aa0a68f","resolution":{"observed_at":"2026-08-10T10:28:43.935946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.27624","last_updated":"2026-04-30T09:13:08Z","snapshot_observed_at":"2026-07-06T23:13:02.921765Z","submitted_at":"2026-04-30T09:13:08Z","title":"Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.27624","snapshot_observed_at":"2026-08-10T10:28:40.674371Z","title":"Mapping how llms debate societal issues when shadowing human personality traits, sociodemographics and social media behavior.arXiv preprint arXiv:2604.27624, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.674371Z"},"links":{"cited_paper":"/paper/2604.27624","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:6a4b0e0492ca62b6cec4272475e06c82c70af34845cb986e257f5f80240e497f","observation_id":"7557b040-d2f3-4de6-9340-2e4ce37988c1","resolution":{"observed_at":"2026-08-10T10:28:40.674371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01680","last_updated":"2024-04-19T01:15:16Z","snapshot_observed_at":"2026-08-10T13:10:07.804621Z","submitted_at":"2024-01-21T23:36:14Z","title":"Large Language Model based Multi-Agents: A Survey of Progress and Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01680","snapshot_observed_at":"2026-08-10T10:28:40.679110Z","title":"Large language model based multi-agents: A survey of progress and challenges.arXiv preprint arXiv:2402.01680, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.679110Z"},"links":{"cited_paper":"/paper/2402.01680","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:1414882c8d397e72546291f738e74f3276a36bd246c42cef2e78c9b566c0d6f3","observation_id":"0b1f849f-3da9-4be9-93cd-6449c15ee848","resolution":{"observed_at":"2026-08-10T10:28:40.679110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.920396Z","title":null,"venue":null,"work_id":"3f19614a-bf59-493c-ba84-abf320f54cb5","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.683248Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:e9f581ecb1aeb43db81fc4b544c5ed425169124b31e4e05bf45c6dee148e988f","observation_id":"3661d8af-ed78-4667-bc52-02981617fb83","resolution":{"observed_at":"2026-08-10T10:28:43.924579Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.908435Z","title":"Escaping the jingle-jangle jungle: Increasing conceptual clarity in psychology using large language models.Current Directions in Psychological Science, 35(2):59–65, 2026","venue":null,"work_id":"8f0a7e04-5311-473b-b87a-0bc6e94d7752","year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.686811Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:ab55a0cad9cdd9dbd06819dd7eb1423816621d3a67d407b4d4779f5f40aaade9","observation_id":"6b91d091-394e-499a-87e6-1f173e29c6f6","resolution":{"observed_at":"2026-08-10T10:28:43.912765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.690763Z","title":"Lost in the middle: How language models use long contexts.Transactions of the Association for Computational Linguistics, 12:157–173, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.690763Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:285f28f3bdcdafbdfb71f6aa18f3d3a47640e29a63c95fbe43938a756479e7a7","observation_id":"91ba97af-fd76-441c-8801-045a6af6f1cf","resolution":{"observed_at":"2026-08-10T10:28:40.690763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.889826Z","title":"Large language models accurately identify decision reasons in verbal reports.Proceedings of the National Academy of Sciences, 123(27):e2526798123, 2026","venue":null,"work_id":"c5f081e4-9af3-4bc5-a61a-05009f4194e3","year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.694807Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:62dab4c1f978dbf03ce186ff067697429f5a5c6e8e42227b5adefc782f09b284","observation_id":"bc40b7b3-e75f-4a8b-9b4b-eadadc14df87","resolution":{"observed_at":"2026-08-10T10:28:43.893799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.878057Z","title":"Evaluating llms for synthetic personas generation: A comparative analysis of personality representation and censorship effects","venue":null,"work_id":"0c4d8d22-6cc8-479b-85da-68e071069fbc","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.698594Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:7f0272a0172963fc6d21c70ef3a5716403059f039eb6e049612332d331c5ea89","observation_id":"0195748e-56b6-457f-8537-4164d33fc240","resolution":{"observed_at":"2026-08-10T10:28:43.882395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10811","last_updated":"2024-06-17T11:06:57Z","snapshot_observed_at":"2026-08-10T22:37:09.415220Z","submitted_at":"2024-02-16T16:35:35Z","title":"Quantifying the Persona Effect in LLM Simulations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10811","snapshot_observed_at":"2026-08-10T10:28:40.702619Z","title":"Quantifying the persona effect in llm simulations.arXiv preprint arXiv:2402.10811, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.702619Z"},"links":{"cited_paper":"/paper/2402.10811","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:6c399e3d30f233a49c275743dc9e055bd3581fba7cee635f9ca367b76d885cc6","observation_id":"f2fd1f7e-038b-42bd-91e4-81fff6675ded","resolution":{"observed_at":"2026-08-10T10:28:40.702619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00555","last_updated":"2025-06-05T03:20:54Z","snapshot_observed_at":"2026-08-07T17:37:05.037079Z","submitted_at":"2025-03-01T16:42:01Z","title":"Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.00555","snapshot_observed_at":"2026-08-10T10:28:40.706927Z","title":"Safety tax: Safety alignment makes your large reasoning models less reasonable.arXiv preprint arXiv:2503.00555, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.706927Z"},"links":{"cited_paper":"/paper/2503.00555","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:743f233f6137a59d0532f727921cfa29cb03551e2e61a20bb267a465ee1f453f","observation_id":"bffd59bc-6fc8-44e6-a78a-210354e90ac3","resolution":{"observed_at":"2026-08-10T10:28:40.706927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.865826Z","title":"Large language models that replace human participants can harmfully misportray and flatten identity groups.Nature Machine Intelligence, 7(3):400–411, 2025","venue":null,"work_id":"7856a970-60e2-4827-988c-60805dd5acca","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.711126Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:40322f7eaa36d25181c3eaecd5556962ee6ed62fc01055d6d9c193321bf15b3a","observation_id":"1c41d8a0-da19-4014-91a7-f20a5ce69f8d","resolution":{"observed_at":"2026-08-10T10:28:43.870155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.852208Z","title":"Large language models are homogeneously creative.PNAS nexus, 5(3): pgag042, 2026","venue":null,"work_id":"0df6eafe-0ff8-4080-878d-591e6b331541","year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.715002Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:80b953a634da844fc944417422d9daa628e1bae7b3893ec9f82449447838fcb4","observation_id":"27c972a1-0342-48a9-ab19-21c4e8091165","resolution":{"observed_at":"2026-08-10T10:28:43.856344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.27618","last_updated":"2026-07-24T16:21:41Z","snapshot_observed_at":"2026-08-05T18:29:31.920749Z","submitted_at":"2026-04-30T09:08:58Z","title":"Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs","version":2},"cited_work":{"arxiv_id":"2604.27618","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.27618","snapshot_observed_at":"2026-08-10T10:28:41.908089Z","title":"Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs","venue":"cs.AI","work_id":"fbb33a38-97a4-4927-b484-941d3e7cc61b","year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.718671Z"},"links":{"cited_paper":"/paper/2604.27618","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:37ffc3280fdc76efa9db57df292fa62ebdb3118e9c2cb1a40f48ce96cca935a6","observation_id":"33f47695-95a6-4954-bd77-4390877bf431","resolution":{"observed_at":"2026-08-10T10:28:41.980560Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.841522Z","title":null,"venue":null,"work_id":"ba0b3c61-6997-40a8-8426-ceb32e717a61","year":2016},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.722974Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:57fde5f6848bff97f60481f2dc6dfbdcfbdac410d379623f5bdba4c51e9fb5ad","observation_id":"d692acfa-3335-462c-88dc-b7cc69419ebd","resolution":{"observed_at":"2026-08-10T10:28:43.844790Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.829995Z","title":null,"venue":null,"work_id":"3f13e362-2dce-4b89-a3b7-61f2ded68734","year":2015},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.726854Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:bda690678aa3c00fecbeae24f71e9751b6e2b8b0246d255816a7a104c23bafa3","observation_id":"1a04c9dc-41ab-4c93-a4d9-e615e8985bd4","resolution":{"observed_at":"2026-08-10T10:28:43.834051Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.675164Z","title":"A diagnostic meta-analysis of the patient health questionnaire- 9 (phq-9) algorithm scoring method as a screen for depression.General hospital psychiatry, 37(1):67–75, 2015","venue":null,"work_id":"661957ea-0d31-491a-90c5-36308cd75c67","year":2015},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.730776Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:663e59ce2ec4bbd16664f2eb67a9f6bc57b0d074b7187cec7747bce8336fd340","observation_id":"857aa70d-d18a-4234-9f86-750102770959","resolution":{"observed_at":"2026-08-10T10:28:43.776578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.425161Z","title":"Using network science to analyze concept maps of psychology undergraduates.Applied Cognitive Psychology, 33(4):662–668, 2019","venue":null,"work_id":"ff5ed21a-ad0d-4726-8c9d-7f56ec0b0d43","year":2019},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.734200Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:0b984db840e20d4e3a52b9a785bb1d96064a4a2c4a74ef8193e70f293e9d6a06","observation_id":"81875dbc-cac9-408b-84c6-ac40135d7626","resolution":{"observed_at":"2026-08-10T10:28:43.571108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.737890Z","title":"Cognitive networks for knowledge modeling: A gentle introduction for data-and cognitive scientists.Wiley Interdisciplinary Reviews: Cognitive Science, 17(2):e70026, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.737890Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:29f1f225cc21ccdf4b740d6843e0ae28406c73718995ee062f1d84dbebdee2f5","observation_id":"be89c6d0-da48-4f50-9794-9aaa9e43e25a","resolution":{"observed_at":"2026-08-10T10:28:40.737890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.385788Z","title":"Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics.Complexity, 2019(1): 2108423, 2019","venue":null,"work_id":"c97d8030-d379-44b4-8e01-1485d3d962c7","year":2019},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.796393Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:4d0136d4495ff4aab4a9d9cb12209ba12688e0404f6d90568bf28727e9895720","observation_id":"2cf5ef69-a2a8-4090-87e4-44f5080a68b0","resolution":{"observed_at":"2026-08-10T10:28:43.389552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:40.924130Z","title":"spreadr: An r package to simulate spreading activation in a network.Behavior Research Methods, 51(2):910–929, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.924130Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:fb4c0e41d287f7200930d2a283ba1338ed98564298cc829eb1bf909b16fb683d","observation_id":"ad66a87e-3ba6-4476-af18-d14855be8861","resolution":{"observed_at":"2026-08-10T10:28:40.924130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.368528Z","title":"Using network science in the language sciences and clinic.International journal of speech-language pathology, 17(1):13–25, 2015","venue":null,"work_id":"f476a4c3-65b5-4f7c-bfe3-9986fa009ca3","year":2015},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.988974Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:49c14a7be6b867b54fefa1fbd6109482397ea05938de62528145b6cc14871703","observation_id":"cc4f886b-3102-401d-b6be-d27832c9d47a","resolution":{"observed_at":"2026-08-10T10:28:43.371992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.088323Z","title":"Crowdsourcing a word–emotion association lexicon.Computational intelligence, 29(3):436–465, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.088323Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:00af62ee9fb1276a3cf432108cef356b0384015ac2081d1a04dfa031a6981a3a","observation_id":"91a1eb29-f1f2-4758-b868-8b698a89a4dc","resolution":{"observed_at":"2026-08-10T10:28:41.088323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.350311Z","title":"Affective biases in english are bi-dimensional.Cognition and Emotion, 29(7):1147–1167, 2015","venue":null,"work_id":"958b2b6a-5019-4a5d-82cd-f7ff2029b918","year":2015},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.093035Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:d675cc27a1afc428e6e009d29c0e60143174f9acd6f9f5244112f5e406f1525d","observation_id":"c3ca5b3c-1cab-4783-9fc3-621670bcd5d6","resolution":{"observed_at":"2026-08-10T10:28:43.354641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.338916Z","title":"A perspective on explainable artificial intelligence methods: Shap and lime.Advanced Intelligent Systems, 7(1):2400304, 2025","venue":null,"work_id":"30300447-6923-49b3-b917-e0b8f131188e","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.096792Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:652003378c42a3352633591c45ae1785a3b33b618a260e1eba7e9efa5edae017","observation_id":"251bc3cc-f736-410d-a216-a7bbd23c4477","resolution":{"observed_at":"2026-08-10T10:28:43.342393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.100002Z","title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature machine intelligence, 1(5):206–215, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.100002Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:024c8f0ec78ad45931d68e36773c8f25cb2e2aa175aee6c88109f55349754ddb","observation_id":"85be87a0-6229-45d6-8f0e-431e1d57c138","resolution":{"observed_at":"2026-08-10T10:28:41.100002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.321357Z","title":"Personality traits in large language models.Prepritnt, 2023","venue":null,"work_id":"a1db94c4-6cbc-4685-91e9-67a716055537","year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.104154Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:ccfafad839715e844cef6401ac01765bd586cc17eec803b460d11f88482976d2","observation_id":"8f0a3023-d158-46bb-9b37-b2af2e28e8e9","resolution":{"observed_at":"2026-08-10T10:28:43.325525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.310566Z","title":"The satisfaction with life scale.Journal of personality assessment, 49(1):71–75, 1985","venue":null,"work_id":"0acca1c8-c2cd-46e9-ba5a-0dfd36dcefe6","year":1985},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.108383Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:e18e6051308c0baf4c6cf55a440bb206e2b276ed31e349761f35a01659753021","observation_id":"1ea48700-1aa7-4c0b-baf1-80d0c63f041f","resolution":{"observed_at":"2026-08-10T10:28:43.314584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.298979Z","title":"Screening for depressive disorders in patients with skin diseases: a comparison of three screeners.Acta dermato-venereologica, 85(5):414–419, 2005","venue":null,"work_id":"d1d3efff-6fbb-4b00-8627-5465e13a3192","year":2005},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.111998Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:c546b1a04d92b261849f50148da209f15a2d7318c60b644bcbf059936d1e55b1","observation_id":"2988692f-c7c7-4cce-b445-8366366199a9","resolution":{"observed_at":"2026-08-10T10:28:43.303080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.287079Z","title":"Advanced natural-based interaction for the italian language: Llamantino-3-anita, 2024","venue":null,"work_id":"227d1fde-0aee-414f-b24f-88e71b4c20ea","year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.115162Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:a4a5b4de1960e0dfa1c0039d1cb12419c5081fb165a82e91557891a26f9204f4","observation_id":"5a4e414b-f635-4e02-ad16-ca90fa87eca4","resolution":{"observed_at":"2026-08-10T10:28:43.290561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-10T10:28:41.119152Z","title":"Qwen3 technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.119152Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:07a65f849995c50f8af1452164708c00fc0cdd7b674774c105ba918adca64f7a","observation_id":"d365e7c6-ff3a-4791-ae00-f13eeb2bbd2a","resolution":{"observed_at":"2026-08-10T10:28:41.119152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10925","last_updated":"2025-08-08T19:24:38Z","snapshot_observed_at":"2026-08-01T16:27:35.664983Z","submitted_at":"2025-08-08T19:24:38Z","title":"gpt-oss-120b & gpt-oss-20b Model Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10925","snapshot_observed_at":"2026-08-10T10:28:41.123359Z","title":"gpt-oss-120b & gpt-oss-20b model card, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.123359Z"},"links":{"cited_paper":"/paper/2508.10925","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:6ff59e86e879375132688a0211147ee0a2ee85ffd311e455a9b10584c892affe","observation_id":"87e3f56a-c0a5-4971-9ed0-2da3ed65b15c","resolution":{"observed_at":"2026-08-10T10:28:41.123359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.13961","last_updated":"2026-04-14T15:12:44Z","snapshot_observed_at":"2026-08-07T08:18:31.274999Z","submitted_at":"2025-12-15T23:41:48Z","title":"Olmo 3","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.13961","snapshot_observed_at":"2026-08-10T10:28:41.127297Z","title":"OLMo 3: Open language models.arXiv preprint arXiv:2512.13961, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.127297Z"},"links":{"cited_paper":"/paper/2512.13961","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:d85c42091e1f4586a89c65899c22ee046aaa92339a81a77d37030247767698c9","observation_id":"4ee601a7-7b0e-4051-b375-bac761ac13a8","resolution":{"observed_at":"2026-08-10T10:28:41.127297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.131797Z","title":"Nemotron 3 nano: Open, efficient mixture-of-experts hybrid mamba-transformer model for agentic reasoning.arXiv preprint arXiv:2512.20848, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.131797Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:34fc4872221585757349049b96a2fcbd20f460a33e35d60b51ea05cb399e1b0b","observation_id":"1f65dbe9-8136-454b-9a87-ac1a57dd728b","resolution":{"observed_at":"2026-08-10T10:28:41.131797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.135387Z","title":"Testing theory of mind in large language models and humans.Nature Human Behaviour, 8(7):1285–1295, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.135387Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:b3e1bd9f91e15adc1acf472d5dcff0b6964f2f854902195790b631d66652a554","observation_id":"772ce7ed-0677-4896-8dbb-8c28eb1a78ea","resolution":{"observed_at":"2026-08-10T10:28:41.135387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.139114Z","title":"Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception.Cognition, 13(1):103–128, 1983","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.139114Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:b47dcc29263297fb9743fc1f2a5e4ebe36675e226f95411f3d9d561f038d9543","observation_id":"2891d5a2-9566-40e1-baa6-920498691732","resolution":{"observed_at":"2026-08-10T10:28:41.139114Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.262415Z","title":"Comparative performance of large language models in emotional safety classification across sizes and tasks.Frontiers in Artificial Intelligence, 8:1706090, 2025","venue":null,"work_id":"46cd1cfe-ae98-41e3-bbae-48ee485c348f","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.143105Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:e8daca0f51f8d836bb4c034e0bb8624df65155083d02309f87e52a45fca4195c","observation_id":"286ce5b3-ab85-48c7-be3f-2b2e6436d7ee","resolution":{"observed_at":"2026-08-10T10:28:43.266097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:43.168868Z","title":"The cost of thinking is similar between large reasoning models and humans.Proceedings of the National Academy of Sciences, 122(47):e2520077122, 2025","venue":null,"work_id":"07778821-da92-4bb6-96e7-4a2ebf19521e","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.146839Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:3877c0fcdc119e002471a0513ce60608fff54d61ac76403c5fa1675d4b75450e","observation_id":"5e571775-b1df-4571-8df4-7ea977499722","resolution":{"observed_at":"2026-08-10T10:28:43.254452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.150974Z","title":"Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.150974Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:9eb98e6b390c9480d4f72b79acc02be151c786978cfb80c5f97e5b5997af0694","observation_id":"7ba107b5-a3a3-4f2c-b27e-20a9e659d7a5","resolution":{"observed_at":"2026-08-10T10:28:41.150974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.959067Z","title":"Leveraging llm respondents for item evaluation: A psychometric analysis.British Journal of Educational Technology, 56(3):1028–1052, 2025","venue":null,"work_id":"7fecc389-ef03-45a3-9195-13ee8ccd907f","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.154805Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:85f6e74a76054cb9e9de56f4adb38e1f31d7fca936007505fa164bf731623521","observation_id":"0f831512-5a67-44a4-8b76-a2d167a564b3","resolution":{"observed_at":"2026-08-10T10:28:43.048716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.926151Z","title":"Covid-19 pandemic and lockdown measures impact on mental health among the general population in italy.Frontiers in psychiatry, 11:550552, 2020","venue":null,"work_id":"dc2cc455-0ef2-486e-b152-44b273aecb7e","year":2020},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.158862Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:42533a7f39115f57273eac8d833995cd2cb7d082361d7cc5ece43e925d808406","observation_id":"da468584-2257-417e-8976-1dbdbef141d2","resolution":{"observed_at":"2026-08-10T10:28:42.930363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.915127Z","title":"Psychological distress among italians during the 2019 coronavirus disease (covid-19) quarantine","venue":null,"work_id":"728fb371-bfa1-45c4-b51d-f12d5c62ca98","year":2019},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.162730Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:b2f8fa31ebca6a62504398e82f2a711dd9f2aed2d162b29427634afcbac454c2","observation_id":"a89d959a-ddba-4516-8636-78e6f11820b1","resolution":{"observed_at":"2026-08-10T10:28:42.918955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.904036Z","title":"Text-mining forma mentis networks reconstruct public perception of the stem gender gap in social media.PeerJ Computer Science, 6:e295, 2020","venue":null,"work_id":"376d0717-35e3-4cb4-a8bf-fbdbd756c4fc","year":2020},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.167595Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:4f0e448f40b1275fcdc876f1cfa7d5e4576bc194aba4540c3b779bb07e165efe","observation_id":"86122838-c440-46d0-b908-c99a2f9782a7","resolution":{"observed_at":"2026-08-10T10:28:42.907785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.06387","last_updated":"2025-05-09T19:22:02Z","snapshot_observed_at":"2026-08-07T15:48:20.624808Z","submitted_at":"2025-05-09T19:22:02Z","title":"Textual forma mentis networks bridge language structure, emotional content and psychopathology levels in adolescents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.06387","snapshot_observed_at":"2026-08-10T10:28:41.171706Z","title":"Textual forma mentis networks bridge language structure, emotional content and psychopathology levels in adolescents.arXiv preprint arXiv:2505.06387, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.171706Z"},"links":{"cited_paper":"/paper/2505.06387","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:51b54481116678bc71d19f33bb84bfef5298311260916db9b6c6d533c6a607ff","observation_id":"1f3fcafb-e9b8-4715-87ed-25e69985d074","resolution":{"observed_at":"2026-08-10T10:28:41.171706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.215131Z","title":"Cognitive networks for knowledge modelling: A gentle tutorial for data-and cognitive scientists.PsyArXiv Preprints, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.215131Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:804cdc6acb689db35247a46d0651e9b6d6490702e6d88bc717391538811da4f6","observation_id":"3ee080ee-67d9-4ec1-8b8a-829736ccda8a","resolution":{"observed_at":"2026-08-10T10:28:41.215131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.885703Z","title":"Estimating the number of communities in a network.Physical review letters, 117(7):078301, 2016","venue":null,"work_id":"b7779fb1-46e4-4fde-a62f-faf12db4fe4d","year":2016},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.245231Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:a4cacf07de407322f3d62de5c719885131422ea85f42a05c7ff77afe7f4305f6","observation_id":"8e281c28-ed1d-4326-9750-9fe37a49411e","resolution":{"observed_at":"2026-08-10T10:28:42.889918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.874583Z","title":"Ysocial: An artificial intelligence powered social media virtual twin","venue":null,"work_id":"5a788e15-b58d-4e04-aca2-0fb3d9e4fbf9","year":2026},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.283877Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:0e9063fc179153d1103c369d5651e7cc91427183bcbab4883db558414b9d221c","observation_id":"578bd05d-672c-4cbf-a7a8-6125bcb0147d","resolution":{"observed_at":"2026-08-10T10:28:42.878424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.863002Z","title":"Forma mentis networks quantify crucial differences in stem perception between students and experts.PloS one, 14(10):e0222870, 2019","venue":null,"work_id":"60ae7107-da6b-4f37-ba03-921ae8ebbf73","year":2019},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.338208Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:aff7b45676cb12651e6210e8566716e3c046f5f82a653e049afb840be3bca332","observation_id":"fe62c558-100a-4e5f-beb5-ac322a7d5d49","resolution":{"observed_at":"2026-08-10T10:28:42.867283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.851159Z","title":"A general psychoevolutionary theory of emotion","venue":null,"work_id":"a0e532f5-ef31-40d5-b44b-c7b70439870c","year":1980},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.448700Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:d83678b2c5c1bb0c4fbda4ba8e1d29694dd0d870c7a6b9dd9d123262f02ad879","observation_id":"40693c3f-35c8-4506-a55a-ee462a0e5c70","resolution":{"observed_at":"2026-08-10T10:28:42.854579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.536895Z","title":"Random forests.Machine learning, 45(1):5–32, 2001","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.536895Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:91cceb3cab46444db396836bbd2f70ad2382f8815fe8447eb702ab8570960b77","observation_id":"49ad7173-b992-4d7b-ae56-9b56fc8760f5","resolution":{"observed_at":"2026-08-10T10:28:41.536895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.541057Z","title":"Pedregosa, G","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.541057Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:94fc890485a65f3e4b4c3f4e390677ca1c9a523d4547c1019d2253eefbc07b0c","observation_id":"2b9d3fbc-ce7f-4038-96ca-12c050600a63","resolution":{"observed_at":"2026-08-10T10:28:41.541057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.544131Z","title":"A unified approach to interpreting model predictions.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.544131Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:5352ba5409b9bfd7be2a0c0e81c103e97f78a5d2875a665c3a280fe766baa84f","observation_id":"5a613955-6238-4864-8e0e-a9441b5e3d53","resolution":{"observed_at":"2026-08-10T10:28:41.544131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.547989Z","title":"From local explanations to global understanding with explainable ai for trees.Nature machine intelligence, 2(1):56–67, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.547989Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:dd366571690d268e563f9e869ed3468b243cee3a71d4ab4a7326b448287f87b3","observation_id":"ae93cdf0-160b-4d3a-bc17-2b7237749f2c","resolution":{"observed_at":"2026-08-10T10:28:41.547989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.812678Z","title":"Do models of mental health based on social media data generalize? InFindings of the association for computational linguistics: EMNLP 2020, pages 3774–3788, 2020","venue":null,"work_id":"517710ae-7d11-48d2-a792-48b5e8d31cbc","year":2020},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.551717Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:c00ddbf4d95f3542261823aac9e579a1a9d9f6ac91dd2a5a6f8ccae6a9678d37","observation_id":"e387df7d-06e3-4374-bf37-cc2f1f828ef7","resolution":{"observed_at":"2026-08-10T10:28:42.816900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.767865Z","title":"The androids corpus: A new publicly available benchmark for speech based depression detection.Depression, 47:11–9, 2023","venue":null,"work_id":"79652b97-c5b4-4f24-95f9-4d693a62b8fa","year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.555012Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:6de0168bda9d10557ae2574c32f34e3c88939f8f2921503a3ce785480164e3eb","observation_id":"85d22da5-4315-441d-94e3-96ab2079a945","resolution":{"observed_at":"2026-08-10T10:28:42.804327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.558761Z","title":"Robust speech recognition via large-scale weak supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.558761Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:51036bc3417d0aee9369e2e43ae63be75db45b601933c077cde5acab93ef919d","observation_id":"b01bf2db-b2e6-4285-83e9-ce6baa6d25d0","resolution":{"observed_at":"2026-08-10T10:28:41.558761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.547382Z","title":"Modeling depressive patterns in italian discourse: Insights from natural language processing, 2025","venue":null,"work_id":"c12f8ba7-5a31-4a3e-a4f8-0ceabcbfe215","year":2025},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.562681Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:bcde60901c4868c4e16d261d6d3cd874fd3f8c8dda8a821993337c8345a4960b","observation_id":"55cb1972-f9c1-434a-8f86-8355c5cdfa3c","resolution":{"observed_at":"2026-08-10T10:28:42.649849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.358605Z","title":"Will money increase subjective well-being?Social indicators research, 57 (2):119–169, 2002","venue":null,"work_id":"27996abc-9479-4c4f-8b96-6bcaeecd25e0","year":2002},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.566447Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:30952c91fab382ec599436166afbe86babfcff1f1b69e66e53bad49cb939c641","observation_id":"e83b2c94-7e43-4d47-bedd-932cf4f233f9","resolution":{"observed_at":"2026-08-10T10:28:42.413329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.286195Z","title":"High income improves evaluation of life but not emotional well-being","venue":null,"work_id":"4dea4fc4-9641-44a2-9e5e-285858fbe08c","year":2010},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.570564Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:300970d919e31908361b420c80cf37b77c02eef1a633208f5ecd39cfb9bed5ba","observation_id":"aa28470c-b91c-457d-94ed-c45994a9156f","resolution":{"observed_at":"2026-08-10T10:28:42.309060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.275548Z","title":"Happiness, income satiation and turning points around the world.Nature Human Behaviour, 2(1):33–38, 2018","venue":null,"work_id":"7eecbbb1-a2a4-4185-8590-999082191853","year":2018},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.574477Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:a2d6d0f2acf9b44277b564d4a7766a4f6cd3eb9363535397ba943f70cfae4e0a","observation_id":"cbd0be37-7256-4b46-9fc1-800b333ddbc6","resolution":{"observed_at":"2026-08-10T10:28:42.279502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.265280Z","title":"Sadness as an integral part of depression.Dialogues in clinical neuroscience, 10(3):321–327, 2008","venue":null,"work_id":"3e9322dc-fa5b-4b0f-8ae2-8dba610bc533","year":2008},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.578159Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:30d30f08461df41b654a07a9906cd18a2e6e9500859981d86baa8a44e7303de9","observation_id":"c603d5c2-ce5a-440f-a8c9-2d327a4fb5e7","resolution":{"observed_at":"2026-08-10T10:28:42.268906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.254881Z","title":"American psychiatric association Washington, DC, 2013","venue":null,"work_id":"1629d62b-10c4-47e3-a497-4023c48e7dc8","year":2013},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.581791Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:54e5bf54173d3ab0fb6dcbc16029a22cc1d3b4f0a4ba0c6045f2739813a10a73","observation_id":"5355d85a-64a1-4a37-890c-d2d288cc45b9","resolution":{"observed_at":"2026-08-10T10:28:42.258188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.243767Z","title":"Language use of depressed and depression-vulnerable college students.Cognition & Emotion, 18(8):1121–1133, 2004","venue":null,"work_id":"6af32ae4-2aff-4df4-913a-7b83c863b5b2","year":2004},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.585998Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:f06651864c8e3c8b51e7c09b20445e129df7e8473956c7213c6d0adfd7ee946f","observation_id":"fc151111-4acd-4b00-aff0-7b2cad490c66","resolution":{"observed_at":"2026-08-10T10:28:42.247800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.232301Z","title":"Facebook language predicts depression in medical records","venue":null,"work_id":"bb65a0a4-c650-4eba-83a5-d9b6d6af13bd","year":2018},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.589813Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:f8d6f6d6a85ba9aa28c3f7f48bcc6660bf0de66b8a9c2a4df577ff010dd5ba5c","observation_id":"fcbf0943-85c3-427e-b31c-4aebc65f7b51","resolution":{"observed_at":"2026-08-10T10:28:42.236207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.221373Z","title":"Constructive and unconstructive repetitive thought.Psychological bulletin, 134(2):163, 2008","venue":null,"work_id":"54e0bbf5-4265-4650-8b2e-894ffb61ac3f","year":2008},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.593220Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:08903ce24c67eca026bbaa3219306640dc9228c2e0b8016652a77f7d236f4d5e","observation_id":"8353271d-f910-4e82-9824-080189c87371","resolution":{"observed_at":"2026-08-10T10:28:42.224957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.208878Z","title":"Language-based personality: A new approach to personality in a digital world.Current opinion in behavioral sciences, 18:63–68, 2017","venue":null,"work_id":"bbf4eca9-8f3f-4487-a48e-6edced4f77b7","year":2017},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.597120Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:1ca2fac9b8bacd882c75ee919bd744313a51ac26aa40d39f829ab375a263a5af","observation_id":"a99c1966-41bf-4c41-80f7-a14cab9c2a19","resolution":{"observed_at":"2026-08-10T10:28:42.212581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.195833Z","title":"Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth.PeerJ Computer Science, 6:e255, 2020","venue":null,"work_id":"db5f0d30-332b-4123-ac0a-034056693d4a","year":2020},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.600371Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:233c7219c1536728e08b298ff2df6b2dc074d2a7e5c6f7ef4480db7b3a18c1bf","observation_id":"bafbda36-e1d9-48c2-ae72-3b75ba42e21e","resolution":{"observed_at":"2026-08-10T10:28:42.200465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.183275Z","title":"Rethinking rumination.Perspectives on psychological science, 3(5):400–424, 2008","venue":null,"work_id":"08f17f11-25c3-43f1-9846-95779538692e","year":2008},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.603589Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:9991596a9530ee75d6bea2d87814a4bdfb8eb2599284c43377a564021792aa2d","observation_id":"17c17327-466d-4a05-aa4a-0f748cc4c8cc","resolution":{"observed_at":"2026-08-10T10:28:42.187572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.168674Z","title":"Linking “big” personality traits to anxiety, depressive, and substance use disorders: a meta-analysis.Psychological bulletin, 136(5):768, 2010","venue":null,"work_id":"29171967-5df8-4034-80ec-47e2305d7e72","year":2010},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.607092Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:2589d75e0638b5270550eb77e617b0830a21c37bede063bc24908142ea6245f5","observation_id":"688efb6d-b949-4444-bb31-5f321cf2c2c4","resolution":{"observed_at":"2026-08-10T10:28:42.174390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:42.109406Z","title":"Worry: A cognitive phenomenon intimately linked to affective, physiological, and interpersonal behavioral processes.Cognitive therapy and research, 22(6):561–576, 1998","venue":null,"work_id":"56390700-1bb4-4114-b6a7-adc81818b5e1","year":1998},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.610102Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:5422afadfdf7b3d2f1ec3ceb297c882d98c7beacfd3e12b24b7a009382c04dde","observation_id":"f8b9f734-9610-4284-9b6a-87ffaa85ba0f","resolution":{"observed_at":"2026-08-10T10:28:42.160120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.13988","last_updated":"2024-08-08T07:32:14Z","snapshot_observed_at":"2026-08-10T17:19:32.898759Z","submitted_at":"2023-03-24T13:24:41Z","title":"Machine Psychology","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.13988","snapshot_observed_at":"2026-08-10T10:28:41.613505Z","title":"Machine psychology.arXiv preprint arXiv:2303.13988, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:41.613505Z"},"links":{"cited_paper":"/paper/2303.13988","citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:3475b8509fb2b7859d721ea73303dbd0648c23e6d1295cdde8b87d8f6bdc3e77","observation_id":"6664000f-0e31-445a-8cfb-36887ef7c485","resolution":{"observed_at":"2026-08-10T10:28:41.613505Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.31234/osf.io/7zhvr_v1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T10:28:41.644147Z","title":null,"venue":null,"work_id":"4483d709-5f93-41a0-84d4-42f472e222d3","year":null},"citing_paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics","version":1},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-10T10:28:40.654635Z"},"links":{"citing_paper":"/paper/2608.07316"},"observation_digest":"sha256:0c33bec04462f9c1ebbb521437ba3442fd24838de0da96eb1ca0597add3e1b5d","observation_id":"69507402-af41-4018-8d58-e13d7fc6044c","resolution":{"observed_at":"2026-08-10T10:28:41.648987Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.07316","last_updated":"2026-08-07T15:10:57Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T06:13:57.044116Z","submitted_at":"2026-08-07T15:10:57Z","title":"Natural Language Processing Psychometrics"},"reference_resolution":{"displayed":92,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":40,"verified_exact":2,"verified_fuzzy":49},"total_outbound_references":92},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 0 inbound Pith citation observations for arXiv:2608.07316."}