Pith. sign in

REVIEW 2 cited by

Avoiding a Tragedy of the Commons in the Peer Review Process

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1901.06246 v1 pith:JTSPVNAT submitted 2018-12-18 cs.CY cs.DLcs.LGstat.ML

classification cs.CYcs.DLcs.LGstat.ML
keywords reviewcommonsfieldincentivelongpeerprocessprofessional
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hands-Off or Hands-On? Variation in Area Chair Practices and Implications for AI Support

    cs.HC 2026-08 conditional novelty 6.0 of 10

    Through 27 interviews and a design probe, the study shows area chairs differ substantially in engagement style and are cautiously open to AI assistance that adapts to their workflows.

  2. Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards

    cs.AI 2025-05 conditional novelty 6.0 of 10

    A proposal for a two-stage bi-directional AI conference review system with author feedback and an LLM-generated reference review, paired with digital badges and a reviewer impact score for reviewers.

Pith tools