{"as_of":"2026-08-08T11:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2816e07b28e11d7b4640b503b3a9c04f8e29044142d9efe2dde1eba6591340bb","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T22:21:05.784489Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-09T05:50:28.344451Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.06969","last_updated":"2023-05-12T22:50:44Z","snapshot_observed_at":"2026-08-04T17:05:28.243531Z","submitted_at":"2023-05-11T16:49:22Z","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06969","snapshot_observed_at":"2026-08-05T22:21:05.784489Z","title":"A survey on intersectional fairness in machine learning: Notions, mitigation, and challenges","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.07111","last_updated":"2025-08-09T22:24:40Z","snapshot_observed_at":"2026-08-07T22:29:14.834815Z","submitted_at":"2025-08-09T22:24:40Z","title":"Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-05T22:21:05.784489Z"},"links":{"cited_paper":"/paper/2305.06969","citing_paper":"/paper/2508.07111"},"observation_digest":"sha256:cc79144f6f520acd6e11513857c50d463b1c7fd146a5d94c273a3521bd15b405","observation_id":"63162e01-5fe7-44f1-a946-f6ee07403225","resolution":{"observed_at":"2026-08-05T22:21:05.784489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06969","last_updated":"2023-05-12T22:50:44Z","snapshot_observed_at":"2026-08-04T17:05:28.243531Z","submitted_at":"2023-05-11T16:49:22Z","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","version":2},"cited_work":{"arxiv_id":"2305.06969","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06969","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A survey on intersectional fairness in machine learning: Notions, mitigation, and challenges","venue":null,"work_id":"95910aa2-0264-4635-ba46-e06a4c65e4a0","year":2023},"citing_paper":{"arxiv_id":"2605.01597","last_updated":"2026-05-02T20:11:12Z","snapshot_observed_at":"2026-08-03T00:52:48.762187Z","submitted_at":"2026-05-02T20:11:12Z","title":"Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI","version":1},"reference_index":127,"source":"pdf_text","source_observed_at":"2026-05-08T19:27:18.774649Z"},"links":{"cited_paper":"/paper/2305.06969","citing_paper":"/paper/2605.01597"},"observation_digest":"sha256:8c263bcc5eaea4037f3f269bc099d6ad7340af69eda7527523fd1b2e611b1dae","observation_id":"c7533fed-29d9-4162-a24f-66cbe0059208","resolution":{"observed_at":"2026-05-09T05:50:28.346181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.06969/citation-record","integrity":"/paper/2305.06969/integrity","json":"/paper/2305.06969/citation-record.json","paper":"/paper/2305.06969"},"outbound":[],"paper":{"arxiv_id":"2305.06969","last_updated":"2023-05-12T22:50:44Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T17:05:28.243531Z","submitted_at":"2023-05-11T16:49:22Z","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.06969."}