Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1611.04135.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:31:12.022540Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-30T08:24:26.435850Z
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 a5fa1a09-5fc6-470c-a603-c8b32c684ae5 · inbound
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History? cites this paper.
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History? Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:31:12.022540Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a873fdf8-750d-4447-8731-c285f10ce86c · inbound
Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models cites this paper.
Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Reference 96
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T08:24:26.437065Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.