Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Intersectional Definition of Fairness
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1807.08362.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T23:35:42.680314Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z
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 b512d5ff-cb96-4239-a582-af833ebb7998 · inbound
The Science of Evaluating Foundation Models cites this paper.
The Science of Evaluating Foundation Models An Intersectional Definition of Fairness
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T23:35:42.680314Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2cffe8b-a5d7-4789-9d4b-479743140e8d · inbound
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models cites this paper.
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models An Intersectional Definition of Fairness
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:10:49.022731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7898b685-8941-4846-af3a-f920316e0060 · inbound
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program cites this paper.
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program An Intersectional Definition of Fairness
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T18:15:42.719287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.