Paper Citation Record · LEDGER

Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2111.07997.

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

pith.paper-citation-record.v1
2111.07997 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:56:04.587174Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T13:54:43.741169Z

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 26e5f708-6f9f-486a-88d8-cf82bb8e281f · inbound

PaLM 2 Technical Report cites this paper.

PaLM 2 Technical Report Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

Reference 130

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:59:27.230979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6affca1d-c2cc-4b78-977f-c9391620e995 · inbound

Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism cites this paper.

Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:56:04.587174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eecde97f-674b-4319-b604-ac0b177038a7 · inbound

Data-Driven and Participatory Approaches toward Neuro-Inclusive AI cites this paper.

Data-Driven and Participatory Approaches toward Neuro-Inclusive AI Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:29.888238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53b8762d-378e-4b04-9b68-c0d5d8d975c1 · inbound

SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models cites this paper.

SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:52:42.298060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4fec430a-44a4-4eff-a1d1-e4eac0714209 · inbound

From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives? cites this paper.

From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives? Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:33:42.023077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d712bebc-435f-4e46-9453-77a526bcc317 · inbound

Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes cites this paper.

Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T13:54:43.742609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.