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Paper Citation Record · LEDGER

Scaling Truth: The Confidence Paradox in AI Fact-Checking

As of 17 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2509.08803.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.08803 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:09:16.969634Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

66 of 66 outbound references displayed

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  • verified fuzzy0
  • unresolved59
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External citation measurements

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Outbound references

Observation 4725a002-4cd4-483a-b205-88c1e52850a8 · outbound

This paper cites Nature Human Behavior 8(9), 1643–1655 (2024) https://doi.org/10.1038/s41562-024-01959-9.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Nature Human Behavior 8(9), 1643–1655 (2024) https://doi.org/10.1038/s41562-024-01959-9

Reference 1

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Observation f93dee2d-f7ff-41e9-9297-29795a824ecc · outbound

This paper cites Journalism0(0) (2025) https://doi.org/10.1177/14648849251317150.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Journalism0(0) (2025) https://doi.org/10.1177/14648849251317150

Reference 2

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Observation 14768655-fbf3-4216-afb3-d17cd8eb511f · outbound

This paper cites Proc Natl Acad Sci U S A121(50), 2322823121 (2024) https://doi.org/10.1073/pnas.2322823121.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Proc Natl Acad Sci U S A121(50), 2322823121 (2024) https://doi.org/10.1073/pnas.2322823121

Reference 3

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Observation 1cb6b288-5e82-4145-b9c2-04017d20ffee · outbound

This paper cites Technical Report 24-93, Swiss Finance Institute (February 2024).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Technical Report 24-93, Swiss Finance Institute (February 2024)

Reference 4

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Observation 89fe2462-c2d7-4df1-9b1f-645ad302f217 · outbound

This paper cites Proceedings of the National Academy of Sci- ences120(30), 2305016120 (2023) https://doi.org/10.1073/pnas.2305016120 https://www.pnas.org/doi/pdf/10.1073/pnas.2305016120.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Proceedings of the National Academy of Sci- ences120(30), 2305016120 (2023) https://doi.org/10.1073/pnas.2305016120 https://www.pnas.org/doi/pdf/10.1073/pnas.2305016120

Reference 5

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Observation 67b524da-4149-4e0b-9c4a-96abc47e1ca1 · outbound

This paper cites AI & Society (2025) https://doi.org/10.1007/ s00146-025-02199-9.

Scaling Truth: The Confidence Paradox in AI Fact-Checking AI & Society (2025) https://doi.org/10.1007/ s00146-025-02199-9

Reference 6

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Observation b4b9de65-8e8e-41d1-b2d6-328529cb877e · outbound

This paper cites Language Models are Few-Shot Learners.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Language Models are Few-Shot Learners

Reference 7

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Observation aa2014ff-8725-4b2b-871b-58640c5c354d · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 8

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Observation 14fa86f1-e1ea-425b-84bb-0ecbd3818fac · outbound

This paper cites Accessed: 2024-11-15 (2024).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2024-11-15 (2024)

Reference 9

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Observation 37030247-5411-48f5-9ea8-9e42de38934c · outbound

This paper cites Accessed: 2024- 12-07 (2024).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2024- 12-07 (2024)

Reference 10

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Observation be445c0c-47b4-4dcc-8373-6a4a636018aa · outbound

This paper cites Accessed: 2024-11-12 (2024).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2024-11-12 (2024)

Reference 11

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Observation 27a94853-56d5-4d99-af71-2d921a7edc8b · outbound

This paper cites Nature637(8047), 778–780 (2025) https://doi.org/10.1038/ d41586-025-00068-5.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Nature637(8047), 778–780 (2025) https://doi.org/10.1038/ d41586-025-00068-5

Reference 12

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Observation acea3260-658d-4052-a445-fc01b11844ea · outbound

This paper cites Scientific Reports14, 16375 (2024) https://doi.org/10.1038/s41598-024-66708-4.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Scientific Reports14, 16375 (2024) https://doi.org/10.1038/s41598-024-66708-4

Reference 13

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Observation ccedecf5-9e82-4ffc-a0b5-39e3de2fee96 · outbound

This paper cites ACM Computing Surveys55(12), 1–38 (2023) https://doi.org/10.1145/3571730.

Scaling Truth: The Confidence Paradox in AI Fact-Checking ACM Computing Surveys55(12), 1–38 (2023) https://doi.org/10.1145/3571730

Reference 14

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Observation 84cd5935-6168-4d20-ba99-8e7ea34d89da · outbound

This paper cites Nature Machine Intelligence7(2), 221–231 (2025) https://doi.org/10.1038/ s42256-024-00976-7.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Nature Machine Intelligence7(2), 221–231 (2025) https://doi.org/10.1038/ s42256-024-00976-7

Reference 15

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Observation 63f5e931-58e9-4d7e-b120-2aaf921ac7f1 · outbound

This paper cites Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

Reference 16

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Observation 899d8b12-0c45-4de3-b678-de93c362e612 · outbound

This paper cites Forbes (2025).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Forbes (2025)

Reference 17

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Observation e7c3f126-c39e-4d57-bb6b-22283994c8d6 · outbound

This paper cites LLeMpower: Understanding Disparities in the Control and Access of Large Language Models.

Scaling Truth: The Confidence Paradox in AI Fact-Checking LLeMpower: Understanding Disparities in the Control and Access of Large Language Models

Reference 18

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Observation 2e076836-322e-4468-952e-7d4f2c8c099b · outbound

This paper cites Accessed: 2025-02-15.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2025-02-15

Reference 19

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Observation fadc1c07-7ba9-4d2c-8854-f8f4ec47721d · outbound

This paper cites In: Proceedings of the 30th International Conference on Intelligent User Interfaces.

Scaling Truth: The Confidence Paradox in AI Fact-Checking In: Proceedings of the 30th International Conference on Intelligent User Interfaces

Reference 20

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Observation ba24cba5-f582-49b1-8835-98dead0912b2 · outbound

This paper cites The Woman Worked as a Babysitter: On Biases in Language Generation.

Scaling Truth: The Confidence Paradox in AI Fact-Checking The Woman Worked as a Babysitter: On Biases in Language Generation

Reference 21

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Observation f199b31f-43b6-4ace-a982-e962a5e22f10 · outbound

This paper cites Accessed: 2025-04-09 (2025).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2025-04-09 (2025)

Reference 22

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Observation 85e3ff72-b456-440c-8077-298fd3ef2fd5 · outbound

This paper cites Scientific Reports14, 26133 (2024) https://doi.org/10.1038/s41598-024-76900-1.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Scientific Reports14, 26133 (2024) https://doi.org/10.1038/s41598-024-76900-1

Reference 23

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Observation 6760b160-302e-4579-a9e0-0c1667b1508d · outbound

This paper cites Proceedings of the National Academy of Sciences120(11) (2023) https://doi.org/10.1073/pnas.2208839120 2208839120.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Proceedings of the National Academy of Sciences120(11) (2023) https://doi.org/10.1073/pnas.2208839120 2208839120

Reference 24

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Observation 7446cb7c-d73b-4640-b8b5-e2c97b1c96f1 · outbound

This paper cites Science Advances9(26), 1850 (2023) https://doi.org/10.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Science Advances9(26), 1850 (2023) https://doi.org/10

Reference 25

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Observation d90f58c8-fe6f-4dfd-af08-6c2ec88234ac · outbound

This paper cites Accessed: 2025-01-05 (2024).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2025-01-05 (2024)

Reference 26

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Observation e0c872f7-ec9a-46a4-9f86-73b0e669006a · outbound

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Scaling Truth: The Confidence Paradox in AI Fact-Checking PsyArXiv (2023)

Reference 27

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Observation aa6562a5-0d4b-40a6-8200-e446394dd72d · outbound

This paper cites Frontiers in Artificial Intelligence7(2024) https://doi.org/10.3389/frai.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Frontiers in Artificial Intelligence7(2024) https://doi.org/10.3389/frai

Reference 28

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Observation 6861b88c-3610-4746-9590-f1c52d8534ed · outbound

This paper cites Does Prompt Formatting Have Any Impact on LLM Performance?.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Does Prompt Formatting Have Any Impact on LLM Performance?

Reference 29

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Observation 40e3fbb2-fda4-4e6c-851f-7f0e154508c9 · outbound

This paper cites Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

Reference 30

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Observation 21ed6472-26a3-4e89-8808-7837ad1154cc · outbound

This paper cites In: Ku, L.-W., Martins, A., Srikumar, V.

Scaling Truth: The Confidence Paradox in AI Fact-Checking In: Ku, L.-W., Martins, A., Srikumar, V

Reference 31

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Observation 1a570d73-fc95-4b36-99ff-3179e2fb05ca · outbound

This paper cites In: Bouamor, H., Pino, J., Bali, K.

Scaling Truth: The Confidence Paradox in AI Fact-Checking In: Bouamor, H., Pino, J., Bali, K

Reference 32

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Observation d4946116-cdc9-44d5-bfa9-5acfbc6ddeb8 · outbound

This paper cites In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp.

Scaling Truth: The Confidence Paradox in AI Fact-Checking In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp

Reference 33

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Observation bfa75f05-e291-4dbd-93dd-61c8683b2d6a · outbound

This paper cites DA WN.COM (2025).

Scaling Truth: The Confidence Paradox in AI Fact-Checking DA WN.COM (2025)

Reference 34

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Observation b0feee37-bb08-4a7d-86a5-da8daf3f2374 · outbound

This paper cites In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Scaling Truth: The Confidence Paradox in AI Fact-Checking In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Reference 35

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Observation 983749be-ae1f-4483-8a8e-5d40e4c27867 · outbound

This paper cites https://www.semrush.com/blog/ chatgpt-search-insights/.

Scaling Truth: The Confidence Paradox in AI Fact-Checking https://www.semrush.com/blog/ chatgpt-search-insights/

Reference 36

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Observation 4a74b5de-7e4a-4b38-98f6-cf04d15eef72 · outbound

This paper cites Accessed: 2024-11-03 (2024).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2024-11-03 (2024)

Reference 37

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Observation 85734a91-ef0d-400d-8ad9-76e910123d6a · outbound

This paper cites Journal of Personality and Social Psychology77(6), 1121–1134 (1999) https://doi.org/10.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Journal of Personality and Social Psychology77(6), 1121–1134 (1999) https://doi.org/10

Reference 38

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source=pdf_text observed=2026-08-04T20:09:16.654986Z digest=sha256:d29a98989b7f41b340395e5cc7fbbab0da7805fe63e126b73c407e0992287f9b

Observation 53f30c8b-d87f-4842-9c1c-b683d0e9452a · outbound

This paper cites Accessed: 2024-10-02.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2024-10-02

Reference 39

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source=pdf_text observed=2026-08-04T20:09:16.713976Z digest=sha256:e451b07dc00bb63a75fc2b6d2a58cb75afdc444b749ae00b61af6b6e1f93d403

Observation ba0340f9-33c0-4bf6-8bbb-1e4982604ec6 · outbound

This paper cites Mistral 7B.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Mistral 7B

Reference 40

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Observation 1c1942eb-df40-4f62-91d7-bf51e81af6bd · outbound

This paper cites Mixtral of Experts.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Mixtral of Experts

Reference 41

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source=pdf_text observed=2026-08-04T20:09:16.839046Z digest=sha256:e74d243e93cd691442fac1942b900330805e108b8d63ab3ccb68ea151713268d

Observation 0bf7e39b-e6b5-4dd9-adcd-39052e8f6381 · outbound

This paper cites Accessed: 2025-02-09 (2025).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Accessed: 2025-02-09 (2025)

Reference 42

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Observation 7ddc5edc-4dad-413a-9f76-2faee17d1271 · outbound

This paper cites IEEE Trans.

Scaling Truth: The Confidence Paradox in AI Fact-Checking IEEE Trans

Reference 43

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source=pdf_text observed=2026-08-04T20:09:16.890625Z digest=sha256:d680d3520865c7e291cc8edfc1e60499accf7553547107f4f3811c60518b376b

Observation b697b355-56c8-46fc-a310-b7c5eaddbe85 · outbound

This paper cites https://doi.org/10.1109/TEC.1957.5222035.

Scaling Truth: The Confidence Paradox in AI Fact-Checking https://doi.org/10.1109/TEC.1957.5222035

Reference 44

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source=pdf_text observed=2026-08-04T20:09:16.893983Z digest=sha256:7ad3512c19a351f9d6775c6d372d5cf49f2d027022e5441f630628dcccbbd2e4

Observation 7f047c89-d920-4dfb-8929-36d56d9e9195 · outbound

This paper cites Overcoming Common Flaws in the Evaluation of Selective Classification Systems.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Overcoming Common Flaws in the Evaluation of Selective Classification Systems

Reference 45

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source=pdf_text observed=2026-08-04T20:09:16.897171Z digest=sha256:8e2b98bd89a03cb05a78e9075d8b15f46a4217aeeb04062710b4b6a6e8be163c

Observation 2418c629-0792-4380-a047-235bada9e429 · outbound

This paper cites Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models

Reference 46

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source=pdf_text observed=2026-08-04T20:09:16.900567Z digest=sha256:01b14da76379d0bc07506394a5d8236a8c1a92a867281fb78632cd6a2baf7419

Observation d8b0aad3-d0f1-49e0-834e-9dafdbf8cbf4 · outbound

This paper cites F AccT ’22, pp.

Scaling Truth: The Confidence Paradox in AI Fact-Checking F AccT ’22, pp

Reference 47

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source=pdf_text observed=2026-08-04T20:09:16.903772Z digest=sha256:2b7c76d804181750a5517dda2407a112bdd7e507b6d3746e8669ab4b8ec3165c

Observation b26be2e4-e270-49a2-b52f-095bf28420c2 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Scaling Truth: The Confidence Paradox in AI Fact-Checking TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 48

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Observation 0249f30d-ad79-4fca-8717-b3c2c361f155 · outbound

This paper cites https://arxiv.org/abs/2405.04760.

Scaling Truth: The Confidence Paradox in AI Fact-Checking https://arxiv.org/abs/2405.04760

Reference 49

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source=pdf_text observed=2026-08-04T20:09:16.910456Z digest=sha256:5ac0659bebbfdceeb39cfd0faf841ab3e5cc18217a07ddc955ecb35c7c18b2fa

Observation 22f2574d-5286-44d4-a24c-57ee868771d4 · outbound

This paper cites In: Proceedings of the Fourth ACM International Conference on AI in Finance.

Scaling Truth: The Confidence Paradox in AI Fact-Checking In: Proceedings of the Fourth ACM International Conference on AI in Finance

Reference 50

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source=pdf_text observed=2026-08-04T20:09:16.913502Z digest=sha256:c55c1d5d2368af8af9940c23c1e60e2f6efbfe0c66d61e1e4560dc12c813bd7a

Observation e69d5d50-a93b-43e1-8eab-2580920878f0 · outbound

This paper cites Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer

Reference 51

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source=pdf_text observed=2026-08-04T20:09:16.917320Z digest=sha256:68709fb3f74e55299297f16aee58cc97f988eac5dc3d05fde38578a8906d522a

Observation ee1efba3-7a77-4933-832a-7a788aca5f12 · outbound

This paper cites Harvard Data Science Review7(1) (2025).

Scaling Truth: The Confidence Paradox in AI Fact-Checking Harvard Data Science Review7(1) (2025)

Reference 52

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Observation fdc5e92c-e2b0-4ba8-a456-3e4fab109924 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Language Models (Mostly) Know What They Know

Reference 53

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source=pdf_text observed=2026-08-04T20:09:16.924757Z digest=sha256:8a43cb72ffd1fa568acd51e2ac3318e7924529b6318dcecb544171f9bf0004d8

Observation 9e292bac-e7de-4545-be64-a14c4a550d32 · outbound

This paper cites Sub-Saharan Africa,.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Sub-Saharan Africa,

Reference 54

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source=pdf_text observed=2026-08-04T20:09:16.928450Z digest=sha256:b259df20303c50beea4a4a1b8476af28f2f98d0a17084a8cf80a49225cc3a73e

Observation 029f6c62-dcfe-460b-a153-c9e7a1732ec8 · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 56

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Observation e3eb2a6d-b137-4c6b-8902-c98735d5a774 · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 57

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Observation cfc3d92a-d3b9-448d-9da4-5b32f8b5c0f6 · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 58

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Observation 642fed04-6cd7-4bba-92bf-2e223827768e · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 59

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source=pdf_text observed=2026-08-04T20:09:16.943257Z digest=sha256:8449a732582c86b389da2fdb54a466ed0cd2bf26427912ca6404044314ed0476

Observation 3e805131-9379-490d-a486-76ecc9fe5f07 · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 60

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Observation 43f74659-4c8e-416e-87bc-325eb3372d9e · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 61

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Observation f0bbbea9-94af-4016-8a32-a370ee59087b · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 62

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source=pdf_text observed=2026-08-04T20:09:16.953981Z digest=sha256:9e99d1d30d78a64336cad72916d8997d5121db642ea3b8e5ac03af52f6cdf3ba

Observation 321c6ce2-6f81-4ee0-b9c1-4b9df604416a · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 63

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Observation 594336d4-5b4f-44f0-8f4a-98cef3ab5eef · outbound

This paper cites an unresolved cited work.

Scaling Truth: The Confidence Paradox in AI Fact-Checking Unresolved cited work

Reference 64

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Observation f5a7a7c1-517f-46cd-a1e0-311cc31c7704 · outbound

This paper cites There is no credible evidence to support the claim.

Scaling Truth: The Confidence Paradox in AI Fact-Checking There is no credible evidence to support the claim

Reference 65

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source=pdf_text observed=2026-08-04T20:09:16.965096Z digest=sha256:ac39da4dc104ec0e276ec3c4007e7b7175fe6f8e50427b4683d690e5e0d2c36b

Observation 84f4dff9-8dc5-4171-992b-e4e47b9e9161 · outbound

This paper cites This is True.

Scaling Truth: The Confidence Paradox in AI Fact-Checking This is True

Reference 2020

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source=pdf_text observed=2026-08-04T20:09:16.969634Z digest=sha256:834d8448c015dcddcb0d787de12e8797cb0e40b911047882a4db9e54dbb0af3a

Observation 5a75f6fc-7eda-48cc-b079-bb5a9dac38f3 · outbound

This paper cites https: //doi.org/10.1145/3626772.3657914 .https://doi.org/10.1145/3626772.3657914.

Scaling Truth: The Confidence Paradox in AI Fact-Checking https: //doi.org/10.1145/3626772.3657914 .https://doi.org/10.1145/3626772.3657914

Reference 2707

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source=pdf_text observed=2026-08-04T20:09:16.439973Z digest=sha256:669d2bc9db103dcdb3445c0a2583fefc5b68d1a05dd6c23f5f9abf2de11e7018

Pith citing papers

No inbound Pith citation observations are available.