{"as_of":"2026-08-22T08:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:246d90a3e6aeb43f9a0e6a69aee94da2986b3695e982a93f7a94897b3a347dca","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:14:32.495983Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2608.12269/citation-record","integrity":"/paper/2608.12269/integrity","json":"/paper/2608.12269/citation-record.json","paper":"/paper/2608.12269"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:14:33.302973Z","title":"Corruption perceptions index 2024 – ecuador,","venue":null,"work_id":"baa2976c-5cb7-43a2-bac9-772441ee3d71","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.285707Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:bb7b2eb9c5ba693c29b8f535197a633ea13bc7d22c2bc7dc4b2966ff423811ea","observation_id":"83b3bba1-5574-46bf-b9ca-0f8a796ec4be","resolution":{"observed_at":"2026-08-16T00:14:33.309256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.285586Z","title":"Informe de Rendi- cion de Cuentas 2024,","venue":null,"work_id":"7c9f1be8-3583-4d5a-b169-17003c109e56","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.291838Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:663369a81c78c864673024e60194343f65830e67eb46faa54425964c433f67df","observation_id":"56db6b28-4d22-4b33-8935-ab57a2ba7e20","resolution":{"observed_at":"2026-08-16T00:14:33.291506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.270336Z","title":"Ecuador design report 2019– 2021,","venue":null,"work_id":"c3a17678-d192-4181-9917-8e16b2876269","year":2019},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.297851Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:c055a07b5945ccca23d75b43235b0491e51f822935c4d2b173e2a25667fb6eab","observation_id":"cc3a77d4-1113-4aa1-9e3f-449e4671d0e5","resolution":{"observed_at":"2026-08-16T00:14:33.274966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.255315Z","title":"Towards a methodology for analyzing pub- lic procurement data from kapak’s database,","venue":null,"work_id":"a6daa102-8f06-4c4a-b0a5-0b4e8c661265","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.303818Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:0e687cd0988caf74e742dc79122b09919702fd1391972e2e4c5174c8d0078872","observation_id":"78b971df-20ab-4281-925f-0736938158b5","resolution":{"observed_at":"2026-08-16T00:14:33.259951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.237417Z","title":"Kapak: Transparency in Public Procurement – Methodology","venue":null,"work_id":"eac17dd9-4a62-4fb9-825a-380a90b0b860","year":2025},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.309251Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:0ba15c8dc8b331ff20cbc4e230e10e0ec10c36dce6607012e84a92304bdbd71e","observation_id":"049e5f13-cb5a-4c83-8dcf-84d283cd541d","resolution":{"observed_at":"2026-08-16T00:14:33.243058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.222516Z","title":"Deep learning based text classification: A comprehensive review,","venue":null,"work_id":"0dea623e-9a38-4547-80d8-669da602b19a","year":2021},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.314934Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:f1a2cf269a2cd9ffc7ecbe8d4ec225882c1877aea151edc0168fb4b0b3aad6c9","observation_id":"03233528-c3eb-4cbb-ad54-af19d95ef7a4","resolution":{"observed_at":"2026-08-16T00:14:33.227240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.204570Z","title":"Senticnet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis,","venue":null,"work_id":"7b942842-17e9-4ce5-ad82-46b4f01e0f5b","year":2014},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.319956Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:14d8b36326e529ba07d52e9be0fca2ec04a7d1c3bbf5ac4ae21fb15e0f931fe7","observation_id":"d039d670-2793-4462-8aa2-25763045492b","resolution":{"observed_at":"2026-08-16T00:14:33.210654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.188153Z","title":"Sentiment analysis and topic detection of spanish tweets: A comparative study of nlp techniques,","venue":null,"work_id":"d14641ca-699f-4e8e-96c0-d4f30ddd9ffc","year":2013},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.324381Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:27ff2ce1fc484231f4b0310b6a7f33755ba3745ba8ea06da2f853c9181ce8bd4","observation_id":"cbe3e02d-c936-4f62-a738-f030914c8edd","resolution":{"observed_at":"2026-08-16T00:14:33.193872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.167199Z","title":"Clustering of scientific articles using natural language processing,","venue":null,"work_id":"8c1e1bee-cf00-4e48-8301-9ef0df1fe571","year":2022},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.329123Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:ad2504bbb939f3e74d50f43be9ee98de8a3b0abd5b368f40ba5c657cda422ba4","observation_id":"60368cbb-0ea7-4e5c-961d-08beb675a4f7","resolution":{"observed_at":"2026-08-16T00:14:33.176794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.146590Z","title":"Cluster- ing scientific documents with topic modeling,","venue":null,"work_id":"62c7ed71-2a36-4fe9-a5f2-1b5c534fd337","year":2014},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.333497Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:eec546ae78ac31a8cef69c954d29765bfe23225667acf33a1a21adebdc30a936","observation_id":"44e739e5-78ed-4e22-bff7-2dc55bcb9609","resolution":{"observed_at":"2026-08-16T00:14:33.151713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.126032Z","title":"Smart citizen control of public procure- ment in ecuador: Classification of accusatory comments from “sistema oficial de contrataci´ on p´ ublica del ecuador (soce)","venue":null,"work_id":"391d7a87-7642-4b8a-9ed8-1137b09c9bb1","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.339265Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:9907e45711596070a8b2445d9217cc68daffe0fb7290b1018ff661e9bc2183c5","observation_id":"758d2a63-fa66-40b7-ae8d-3aa9620b2e98","resolution":{"observed_at":"2026-08-16T00:14:33.132105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.107894Z","title":"Prediction of public procure- ment corruption indices using machine learning methods,","venue":null,"work_id":"28e04f00-08d7-481d-b5b2-e4cff297aa18","year":2019},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.344331Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:6335b3a1c859561c1f3c6478102b755d7c8fff6db34112d091f162ca06a1d23f","observation_id":"76e1f738-e05a-4535-9416-97ed67eb608e","resolution":{"observed_at":"2026-08-16T00:14:33.114398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.090598Z","title":"Generative ai for anti-corruption and integrity in government: Taking stock of promise, perils and practice,","venue":null,"work_id":"482ef9ea-28e6-47ac-aceb-269c03bdafb3","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.349893Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:e36b8344590100e3d9bcca36e3dbc4e3cf1c1160eb0d98ed15cf27285a70d779","observation_id":"bf665f7e-1f32-4857-80f0-ee4f0307eefd","resolution":{"observed_at":"2026-08-16T00:14:33.097225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01478","last_updated":"2022-12-14T20:17:10Z","snapshot_observed_at":"2026-08-18T07:57:30.325948Z","submitted_at":"2022-10-25T01:22:41Z","title":"A machine learning model to identify corruption in M\\'exico's public procurement contracts","version":2},"cited_work":{"arxiv_id":"2211.01478","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.01478","snapshot_observed_at":"2026-08-16T00:14:32.645900Z","title":"A machine learning model to identify corruption in M\\'exico's public procurement contracts","venue":"cs.CY","work_id":"9d1d408a-a9b4-43d2-88f8-bfd71e78ce2b","year":2022},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.355445Z"},"links":{"cited_paper":"/paper/2211.01478","citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:e097823d02e05b174582af7ccad96e5d64e75260a75c68c3bee6af8ac5d5e162","observation_id":"c14f82dd-8035-4420-951e-b01fc9e77274","resolution":{"observed_at":"2026-08-16T00:14:32.654814Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.072048Z","title":"Survey article: Inter-coder agree- ment for computational linguistics,","venue":null,"work_id":"fdcf350e-7f77-44ad-bea8-f9493a2c20d3","year":2008},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.361663Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:0b37217218bed239e2c41ffbd129cd42d90dfa41d1bf53a77cb2e7a1d397c086","observation_id":"44fe8c61-0d97-45a9-89da-83ac70b7f160","resolution":{"observed_at":"2026-08-16T00:14:33.078500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.054548Z","title":"A review on word embedding tech- niques for text classification,","venue":null,"work_id":"ffbb8d9a-d5d7-49a2-b604-7f19cdae9880","year":2021},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.366039Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:3b1942a0525b472b514ba09347f84e254d72959fbae131e8fd5302584c22ebd3","observation_id":"e4e2081e-4998-4c1e-ba92-ba47a61868d8","resolution":{"observed_at":"2026-08-16T00:14:33.060435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.035577Z","title":"Llama 3.2-1b","venue":null,"work_id":"81fea089-cfbb-4cb5-84a5-4878ece82581","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.370540Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:32340070be3f8d4ce6fd359d7c0943917286d8200f4088c5a8a7b92c1d80598c","observation_id":"45f2632f-e379-427b-88a3-2829d541a1a9","resolution":{"observed_at":"2026-08-16T00:14:33.042779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.019464Z","title":"Roberta: A robustly optimized bert pretraining approach,","venue":null,"work_id":"6030c585-8ccc-453c-a367-6032d4d99aad","year":null},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.375640Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:47b9988389029ae27ab7d2eba8356f13d844607c07725bdb53589494a898bcaa","observation_id":"6b8f91a3-d1e3-4904-ba43-08d5595571cc","resolution":{"observed_at":"2026-08-16T00:14:33.024183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:33.000968Z","title":"Syntactic-aware text classifi- cation method embedding the weight vectors of feature words,","venue":null,"work_id":"6bef0222-7389-4d7c-aa3a-8e977e8ce484","year":2025},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.380103Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:36c3b5d1be25352e901b173d2f535dce1a4c0321cc819186ef79259f303636d9","observation_id":"e7da2e7a-cfaa-424b-8198-8a7bd1984903","resolution":{"observed_at":"2026-08-16T00:14:33.008828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.08587","last_updated":"2025-05-11T04:59:46Z","snapshot_observed_at":"2026-08-16T05:43:34.833045Z","submitted_at":"2024-12-11T18:06:44Z","title":"Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.08587","snapshot_observed_at":"2026-08-16T00:14:32.384232Z","title":"Advancing single and multi-task text classification through large language model fine-tuning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.384232Z"},"links":{"cited_paper":"/paper/2412.08587","citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:cd056c87107f36576f9c30ebc1d1f1d8be886b21c2a506000a6a9c938eb83822","observation_id":"2303287b-6965-43e5-a6e7-6228f3b8921e","resolution":{"observed_at":"2026-08-16T00:14:32.384232Z","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":"2026-08-16T00:14:32.981919Z","title":"Research on patent text classifi- cation based on word2vec and lstm,","venue":null,"work_id":"07552ef3-c76d-4204-bc62-1e3791c8bb0a","year":2018},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.389105Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:414961d564a693e4ccde4c7f069905f377e39fa86e51c69051dd0141c553da2b","observation_id":"3014ccbb-ca76-4588-ba30-57b6b27365be","resolution":{"observed_at":"2026-08-16T00:14:32.987123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.959412Z","title":"Support vector machines and word2vec for text classification with semantic features,","venue":null,"work_id":"ab4d0037-683e-4eea-b0db-f6fc14d4a890","year":2015},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.393599Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:8f130449dbe17256af938783f06ac65858b0118087f0aafda134ccb4d7d600c0","observation_id":"fc6ff4aa-a545-4159-80ba-c901becd42a0","resolution":{"observed_at":"2026-08-16T00:14:32.965046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.937153Z","title":"Detection of fraud in public procurement using data-driven methods: a systematic mapping study,","venue":null,"work_id":"d832086e-d148-47da-9d90-3fd1ff63bbd5","year":2025},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.397991Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:18e02595c59228f19a810798253ea9b7ddf8f61fcfe828a2086f82be2de30ce0","observation_id":"67d104cb-6131-47d8-97c1-f699bcad6e7f","resolution":{"observed_at":"2026-08-16T00:14:32.945241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.915615Z","title":"Sentence-bert: Sentence embed- dings using siamese bert-networks,","venue":null,"work_id":"0a518321-416d-48ab-9ebd-2c78f1e513d8","year":2019},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.402717Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:64a0fea413c8265b43ec43775020e5edd7240a6046a2ab76106da5057c5ed467","observation_id":"5b4767ea-9633-4223-9680-37dae7f83811","resolution":{"observed_at":"2026-08-16T00:14:32.922602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.894239Z","title":"Least squares quantization in pcm,","venue":null,"work_id":"6005ac90-6514-4fbc-b663-fe1c3c208984","year":1982},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.407927Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:a785dd58ae70981a3b16aca6ba677b62da374ff94c091032dab8daf7750c8197","observation_id":"629f67c6-e279-4c4c-9db9-2d0c9b99ad9c","resolution":{"observed_at":"2026-08-16T00:14:32.899048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.878937Z","title":"Gaussian mixture models — scikit-learn 1.4.2 documentation","venue":null,"work_id":"55a481c3-e1c3-490d-832c-bd3c81aa2803","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.412476Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:efa5d14a0f96dd6a3ec37adc0ce71ce6168491461e59c9de4d23292b3c8a26dc","observation_id":"ecc141e5-4f0a-4fe7-b5c9-bdd01bcc754a","resolution":{"observed_at":"2026-08-16T00:14:32.883630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.861229Z","title":"Ebk-means: A clustering tech- nique based on elbow method and k-means in wsn,","venue":null,"work_id":"8817e034-f260-44f5-a99a-6cbdc8a354e3","year":2014},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.417950Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:899dfa7ab8802eda4cdab7dfe3fce0af43346700653193c31c06bd9c5672d577","observation_id":"5da3ea14-16b6-493f-9a63-bb24353d55e1","resolution":{"observed_at":"2026-08-16T00:14:32.866500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.842841Z","title":"Silhouettes: A graphical aid to the interpre- tation and validation of cluster analysis,","venue":null,"work_id":"6983b9aa-4207-42bb-85a6-5bc01df1b4ab","year":1987},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.423196Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:5ddff4a7ef84a1588856af8e268adf94455a6500eb37db620c6311ff1d185e68","observation_id":"481928f8-f52e-4dc5-a7b7-34b197363541","resolution":{"observed_at":"2026-08-16T00:14:32.848240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.825759Z","title":"Word embedding based clustering to detect topics in social media,","venue":null,"work_id":"570db587-3c2b-4a8e-aef5-b72888105879","year":2019},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.427457Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:55ab147e72b945e28c5d9cdc106ce76818993735c907944f11b0a52a36f16d1e","observation_id":"3b45e90d-7002-4086-bdac-ce0b6410d69c","resolution":{"observed_at":"2026-08-16T00:14:32.831286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.432195Z","title":"Smote: Synthetic minority over-sampling technique,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.432195Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:70550e294a9386a37f8ebdf2b7efa6060bb443d8cb1094c62701b9d50e05a7af","observation_id":"7593f1b9-7c4a-470c-aeaa-28b125bf07f3","resolution":{"observed_at":"2026-08-16T00:14:32.432195Z","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":"2026-08-16T00:14:32.790335Z","title":"Stratifiedkfold — scikit-learn 1.6.1 documentation,","venue":null,"work_id":"87204a0a-e67f-4ca1-a202-56d06c40d198","year":2025},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.437234Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:a2c9adc75a6ee30780e3ff80eca57ae2676dd98b6acb2aa0c8ce6ac338440f36","observation_id":"b4afcf9e-9a05-4e6d-b32a-8f3e71be56f2","resolution":{"observed_at":"2026-08-16T00:14:32.804380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.442073Z","title":"Random forests,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.442073Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:bb675b2d2c748b2c35b19d986a1051063cb63b510822979801de59d74dbb3caf","observation_id":"86bbe226-0872-4445-8e3e-734cb146f0a9","resolution":{"observed_at":"2026-08-16T00:14:32.442073Z","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":"2026-08-16T00:14:32.754796Z","title":"Comparison between multinomial and bernoulli na¨ ıve bayes for text classifi- cation,","venue":null,"work_id":"8c289250-fc0d-4ee8-b68d-aff649833f71","year":2019},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.446640Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:02a0dc7e067fe4baa9843d6c5aadfa894f6b27cec7bd662650a49154103a1227","observation_id":"aee370bd-71f9-4558-8e47-dd63bdbae060","resolution":{"observed_at":"2026-08-16T00:14:32.760933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.732251Z","title":"Text classification based on multi-word with support vector machine,","venue":null,"work_id":"2d4770ac-7782-44fd-b0dd-256477bd326e","year":2008},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.451110Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:e1de1ca35f7e132bc5a3d4bd0d9fa12cce4d5bd3af9b2f7b457b062f71b6c727","observation_id":"0cc8d099-34d1-41fa-9fdf-1f62eef2a05a","resolution":{"observed_at":"2026-08-16T00:14:32.740500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03144","last_updated":"2023-05-04T20:53:19Z","snapshot_observed_at":"2026-08-19T07:11:55.489947Z","submitted_at":"2023-05-04T20:53:19Z","title":"Influence of various text embeddings on clustering performance in NLP","version":1},"cited_work":{"arxiv_id":"2305.03144","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.03144","snapshot_observed_at":"2026-08-16T00:14:32.602362Z","title":"Influence of various text embeddings on clustering performance in NLP","venue":"cs.LG","work_id":"cf875f5a-7f00-4a94-8f7d-4b15838219fd","year":2023},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.455865Z"},"links":{"cited_paper":"/paper/2305.03144","citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:a9a1b1d832e168ff80424f2eea8a294c3b148aa8c2b11a25a808e6b59253d6fc","observation_id":"40b64aff-c05c-4008-934a-d8f691770243","resolution":{"observed_at":"2026-08-16T00:14:32.610874Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.714772Z","title":"Glove word embedding and dbscan algorithms for semantic document clustering,","venue":null,"work_id":"de493e9f-ad0f-4673-814b-18da6ce7b720","year":2020},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.460871Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:9e04b7bd649b1c17ccdec98b357f00d9eb131cde5d2af9911a39d62be92c4de9","observation_id":"27442053-e835-49b3-8f93-2c36c2c6c58c","resolution":{"observed_at":"2026-08-16T00:14:32.719966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00422","last_updated":"2025-03-20T10:08:31Z","snapshot_observed_at":"2026-08-16T14:30:40.017845Z","submitted_at":"2023-12-31T08:22:51Z","title":"Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00422","snapshot_observed_at":"2026-08-16T00:14:32.464917Z","title":"Interpreting the curse of dimen- sionality from distance concentration and manifold effect,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.464917Z"},"links":{"cited_paper":"/paper/2401.00422","citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:10334b650cd6ce904ee9611456414b3036ce4df72c022322cf197f729d197b6d","observation_id":"a8fd20e6-339d-4fdb-8808-8ef58c35fce4","resolution":{"observed_at":"2026-08-16T00:14:32.464917Z","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":"2026-08-16T00:14:32.699628Z","title":"Beyond words: A comparative analysis of llm embeddings for effective clustering,","venue":null,"work_id":"8af346e8-acbe-4b1a-8528-4a579709fc1c","year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.472012Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:4247b53756d606b3df9e8030d75bab1b0cbf88af075ab7546cb1c18c055fdf44","observation_id":"56556cd4-a214-464a-95c5-e5a36f9193d3","resolution":{"observed_at":"2026-08-16T00:14:32.704166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.682895Z","title":"Text clustering with large language model embeddings,","venue":null,"work_id":"08a3d199-f15a-4505-ab27-a82243e8c0d3","year":2025},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.479621Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:08496943af07f1ab4802e4d890059bb104245740dc5e7ed2174e8d1f42f43694","observation_id":"6c2def6e-564f-4ad6-8b01-bb204da83836","resolution":{"observed_at":"2026-08-16T00:14:32.687909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T00:14:32.666480Z","title":"On the sentence embeddings from pre-trained language models,","venue":null,"work_id":"14cae950-78fd-4382-a11a-64f4628e3dc3","year":2020},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.484918Z"},"links":{"citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:f2f2e255e76235de4dbee8dc81e1dbf9e3538d52c559a2d9e94833a8642f4f79","observation_id":"170247b7-f926-43b1-ac49-0179c05c20f8","resolution":{"observed_at":"2026-08-16T00:14:32.671471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.08904","last_updated":"2022-08-05T09:33:10Z","snapshot_observed_at":"2026-08-19T16:39:52.367214Z","submitted_at":"2022-02-17T21:35:56Z","title":"SGPT: GPT Sentence Embeddings for Semantic Search","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.08904","snapshot_observed_at":"2026-08-16T00:14:32.490894Z","title":"Sgpt: Gpt sentence embeddings for semantic search,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.490894Z"},"links":{"cited_paper":"/paper/2202.08904","citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:d8587bc8c796fd55b7cfb2105206e03f4608fd65ef8b80ae2b21e6670544328b","observation_id":"6dbcc057-de8f-4547-8128-c1ccb812ebf9","resolution":{"observed_at":"2026-08-16T00:14:32.490894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05961","last_updated":"2024-08-21T22:46:05Z","snapshot_observed_at":"2026-08-20T00:24:39.214064Z","submitted_at":"2024-04-09T02:51:05Z","title":"LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05961","snapshot_observed_at":"2026-08-16T00:14:32.495983Z","title":"Llm2vec: Large language mod- els are secretly powerful text encoders,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T00:14:32.495983Z"},"links":{"cited_paper":"/paper/2404.05961","citing_paper":"/paper/2608.12269"},"observation_digest":"sha256:a99a431fa9ce46384114e225538d4c999bc49191e2ce7fef8027f40f89d9f0a3","observation_id":"fc30bb81-5553-4def-8b27-e6403cad9ed3","resolution":{"observed_at":"2026-08-16T00:14:32.495983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.12269","last_updated":"2026-08-12T17:09:14Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-21T16:58:35.152753Z","submitted_at":"2026-08-12T17:09:14Z","title":"A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":2,"verified_fuzzy":34},"total_outbound_references":42},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2608.12269."}