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Record W2268343888 · doi:10.17705/1cais.03813

Practical Suggestions for Improving Scholarly Peer Review Quality and Reducing Cycle Times

2016· article· en· W2268343888 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications of the Association for Information Systems · 2016
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeer reviewTransparency (behavior)CriticismHarmScrutinyQuality (philosophy)Public relationsTechnical peer reviewMisconductScientific misconductResentmentPolitical scienceProcess (computing)Engineering ethicsInternet privacyLaw and economicsComputer sciencePsychologyLawSociologyMedicineEngineeringEpistemologyAlternative medicine

Abstract

fetched live from OpenAlex

Scholarly peer review is both central to scientific progress and deeply flawed. Peer review is prejudiced, capricious, inefficient, ineffective, and generally unscientific. Management journals have longer review cycles than journals in other fields. Long cycle times demonstrably harm early-career researchers. Meanwhile, a lack of transparency conceals and facilitates editorial misconduct, and some dismiss legitimate criticism of peer review as unfounded resentment. We can address these problems by eliminating unnecessary reviewing, simplifying the peer review process, introducing author rebuttals, creating an AIS ombudsman, and enforcing the relationship between submitting and reviewing. These problems are, however, entangled with fundamental problems with journals. Ultimately, therefore, we can only fix peer review in conjunction with replacing journals with repositories.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.061
metaresearch head score (Gemma)0.464
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0610.464
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.594
GPT teacher head0.593
Teacher spread0.002 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it