Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deriving Machine Attention from Human Rationales
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1808.09367.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:44:53.107643Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T13:00:04.517912Z
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 2c8a604d-12a1-48c1-bb5d-18192c585144 · inbound
Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment cites this paper.
Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment Deriving Machine Attention from Human Rationales
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:44:53.107643Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4013b4fe-c364-4c58-ba68-e5d6aa9d7bc1 · inbound
SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models cites this paper.
SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models Deriving Machine Attention from Human Rationales
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:15:34.464838Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87872b0b-4df5-4303-9950-0f4e9bac858d · inbound
Can human clinical rationales improve the performance and explainability of clinical text classification models? cites this paper.
Can human clinical rationales improve the performance and explainability of clinical text classification models? Deriving Machine Attention from Human Rationales
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T13:00:04.523516Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.