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Integrity report for Training Ecosystems: A Computational Approach to Uncovering Learning Behavior in Unconventional Contexts

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.30109 · pith:2026:B7FNRRABPW4CPPGTZBCENWI3JP

0Critical
0Advisory
5Detectors run
2026-06-05Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-06-05 03:35:50.211058+00:00
claim_evidence completed v1.0.0 · findings 0 · 2026-06-03 12:48:25.040478+00:00
citation_quote_validity skipped v0.1.0 · findings 0 · 2026-05-31 09:50:44.175248+00:00
shingle_duplication skipped v0.1.0 · findings 0 · 2026-05-29 21:50:04.600260+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-29 06:24:17.923978+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/B7FNRRAB/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.