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Paper Citation Record · LEDGER

Discrimination in Online Ad Delivery

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1301.6822.

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

pith.paper-citation-record.v1
1301.6822 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:01:39.525398Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:01:38.249367Z

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 a90dd2cb-2167-4bf3-9e53-a7c4bf72241e · inbound

Examining Gender Bias in Languages with Grammatical Gender cites this paper.

Examining Gender Bias in Languages with Grammatical Gender Discrimination in Online Ad Delivery

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-14T05:01:39.525398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:01:39.525398Z digest=sha256:10876fc1ddbdf5f3fa16e78604c990e0fd0a58edada6e7ffa83f4852cb46a71f

Observation 5d7a0ef9-3982-4f1b-af33-2955fb755f0c · inbound

Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models cites this paper.

Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models Discrimination in Online Ad Delivery

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:01:38.285478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T11:01:37.933884Z digest=sha256:4b6bf5b207c183bfc49639d47c2aea46a7954b0c54b794fe40fdbd4680e0e6af