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Paper Citation Record · LEDGER

Bias Amplification: Large Language Models as Increasingly Biased Media

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.15234.

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

pith.paper-citation-record.v1
2410.15234 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:33:32.399625Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T00:00:47.868004Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 84010267-e91e-45ec-80ae-69e5a6310d29 · inbound

Measuring Stereotype and Deviation Biases in Large Language Models cites this paper.

Measuring Stereotype and Deviation Biases in Large Language Models Bias Amplification: Large Language Models as Increasingly Biased Media

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-22T00:00:47.869562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T23:57:16.883657Z digest=sha256:481d6b931ceec4f8920087364da93b1cec737ebbdf2bc98e39cfdf923756f0c5

Observation d862d3ea-25f9-431b-afb8-59c192799891 · inbound

Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training cites this paper.

Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training Bias Amplification: Large Language Models as Increasingly Biased Media

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T16:33:32.399625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:33:32.399625Z digest=sha256:e39a216f00dc6f7f47d7253e66b11e983cb9a0ac04a4c1552af60dd3a12ed113