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Paper Citation Record · LEDGER

Fairness guarantee in multi-class classification

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.13642.

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

pith.paper-citation-record.v1
2109.13642 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:34:06.924365Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0d75729d-7998-4bfa-8bfe-ffd6d5b1827e · inbound

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective cites this paper.

Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective Fairness guarantee in multi-class classification

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T05:34:06.924365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:34:06.924365Z digest=sha256:74bcbf027ff919c7e81851ef3c5ff2dd76182bf90170b5768118e47c6e6777c7

Observation 76ed8666-29a4-4253-bf44-7b0aefe18b0a · inbound

Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets cites this paper.

Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets Fairness guarantee in multi-class classification

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-06-26T09:19:17.031003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-26T09:12:19.873337Z digest=sha256:8fb37a9f4f24e1df95f2d991b4fcfa4de699dc87d1e704acfedd4a828ae113e4