Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1906.04571.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:22:18.656484Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-14T23:08:14.442872Z
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 3538814f-81fc-447f-b9f8-c5ed16751932 · inbound
LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models cites this paper.
LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:22:18.656484Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9cf17964-d1bf-4efe-a6a7-d81f3a218cb8 · inbound
Routing Sensitivity Without Controllability: A Diagnostic Study of Fairness in MoE Language Models cites this paper.
Routing Sensitivity Without Controllability: A Diagnostic Study of Fairness in MoE Language Models Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T23:08:14.452071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 31524de0-f3e1-45cf-8d10-5c797c6148c9 · inbound
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution cites this paper.
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Reference 236
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:45:21.110886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.