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Paper Citation Record · LEDGER

Automated Annotation with Generative AI Requires Validation

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2306.00176.

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

pith.paper-citation-record.v1
2306.00176 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T18:18:13.296342Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

35
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 222f21f9-e942-4796-81f8-6699b50b9caa · inbound

Guidelines for Empirical Studies in Software Engineering involving Large Language Models cites this paper.

Guidelines for Empirical Studies in Software Engineering involving Large Language Models Automated Annotation with Generative AI Requires Validation

Reference 100

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:02:52.269813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-18T22:02:36.307598Z digest=sha256:7d7c54c8a85f2b12f605ba9da89a2b40adb4b9a224681626461ee6193a8f1f2d

Observation c1834796-0370-46c4-ac36-3cbe7a26b434 · inbound

Guidelines for Empirical Studies in Software Engineering involving Large Language Models cites this paper.

Guidelines for Empirical Studies in Software Engineering involving Large Language Models Automated Annotation with Generative AI Requires Validation

Reference 100

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:20:31.730528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-25T08:18:18.448122Z digest=sha256:bb6026a8f4488c4f2edd4ea036753aed7be8a78910ca8422f29367ff5199e233

Observation dcf7303c-583f-4dc1-884e-a31c34fd64c2 · inbound

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection cites this paper.

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection Automated Annotation with Generative AI Requires Validation

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:40:27.011321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T13:38:02.450128Z digest=sha256:503ff8ae86abf40c95bdde6310e3a47a0a3246c857ace581902f05889e4b7380

Observation 6cd86732-cf4b-4ac6-872b-c79084c57c03 · inbound

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection cites this paper.

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection Automated Annotation with Generative AI Requires Validation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-12T20:35:52.598345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T20:35:52.598345Z digest=sha256:00c39b76a27e7203d98609261edf68834a540e66b1753c85315334c2ff4ac10e

Observation 919b8be2-0722-4637-b572-983813e721b2 · inbound

LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text cites this paper.

LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text Automated Annotation with Generative AI Requires Validation

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:55:28.694422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T13:54:24.082141Z digest=sha256:28d6ec227b7375d40b49e28fd3ed165cc0d814bed5e13390fe79eee92ec30c5a

Observation 548b3691-7f42-4fc3-903d-e6425ccb8a9b · inbound

LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification cites this paper.

LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification Automated Annotation with Generative AI Requires Validation

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T11:56:27.318769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T04:27:23.053818Z digest=sha256:3299c0f83487153f002475bc4f98b9b22e86c3ea0881db368690ea911eb4fa63

Observation b0d4beca-0fc7-4b20-bce0-2db67f221357 · inbound

How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues cites this paper.

How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues Automated Annotation with Generative AI Requires Validation

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:28:04.054794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-14T22:26:49.010635Z digest=sha256:9eefe6dd72ef8b4cd51e1c52b9cf85a1817d0767a28cfe03b12792ac7663c29c

Observation 6e18edc4-5502-4f03-98b4-8278c1ad5771 · inbound

What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media cites this paper.

What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media Automated Annotation with Generative AI Requires Validation

Reference 38

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T17:17:14.873273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-27T22:08:24.018844Z digest=sha256:016234e654d1ad7970cb3fb366c593b023fea2c5b5dafdaeb086363d291bd064

Observation de043efc-4110-4a25-aaff-0f1d876ab016 · inbound

The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel cites this paper.

The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel Automated Annotation with Generative AI Requires Validation

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:39:49.405965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-26T06:23:58.873694Z digest=sha256:7722f33c74c0d101d8c8af81e7b8178c7c14e88b4bd4b5a7d96ca85e75a70b80

Observation 95bd24b1-d14b-4697-a501-2da8904b2673 · inbound

Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs cites this paper.

Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs Automated Annotation with Generative AI Requires Validation

Reference 49

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T16:25:50.001677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-30T00:40:09.452943Z digest=sha256:91507c70d100385c14c3b75e3a64cc37b90928a070863efb42ea3b3ecde72cf3

Observation c3ce0b92-1081-4b08-b117-5d09ef323d36 · inbound

Talking Politics with Artificial Intelligence cites this paper.

Talking Politics with Artificial Intelligence Automated Annotation with Generative AI Requires Validation

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T03:26:28.410008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-07-02T03:24:16.158417Z digest=sha256:c63a73caf9b1501fe2c8ceb64586945c29a9f1601ef8710c00c624ef7e1ce24d

Observation 64df4f0b-1e9f-4b55-9e2d-b3b396816c3d · inbound

Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy cites this paper.

Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy Automated Annotation with Generative AI Requires Validation

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-07-10T09:06:59.077046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-07-10T08:59:30.965660Z digest=sha256:9d1b8e94dcf3e11650ed6296930201f5e6e7024a0c8e44b50e1f79bad60884d8

Observation 45012712-32f7-4e7b-8ced-0951a8a400c8 · inbound

A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol cites this paper.

A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol Automated Annotation with Generative AI Requires Validation

Reference 56

Resolution
unresolved
no resolver link, observed 2026-07-14T02:31:04.299071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T02:31:04.299071Z digest=sha256:1d35caffcf91f0ff884923b05ae3545394d10008098d984cb827f661283c5a4d

Observation 5ced4911-8529-4d80-a3a4-0cf03fe00d9a · inbound

Auditing Differential Visibility of Political Content on TikTok cites this paper.

Auditing Differential Visibility of Political Content on TikTok Automated Annotation with Generative AI Requires Validation

Reference 138

Resolution
unresolved
no resolver link, observed 2026-08-01T18:18:13.296342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T18:18:13.296342Z digest=sha256:8449161eab70c460d3ea1e04fe0e2a696200146cb13e505f7da98b973bdd10ed