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Roles and Responsibilities for Referees of International Peer Reviewed Journals

2023· preprint· en· W4322487234 on OpenAlexaff
Carol Nash

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeer reviewPublishingRelevance (law)Strengths and weaknessesPsychologyPublic relationsWork (physics)Medical educationPolitical scienceMedicineSocial psychologyEngineeringLaw

Abstract

fetched live from OpenAlex

Scholarly publication in international journals depends on qualified, unbiased and available referees: (1) qualified in their ability to assume each role inherent to being a peer reviewer and in willingly and competently accepting the responsibilities that coincide with these roles; (2) unbiased in focusing on the submitted research content irrespective of their own research programs in judging the submission’s merit; and (3) available to devote time to read and understand the paper, check the accuracy and relevance of references, and write a comprehensive review commenting on the strengths and weaknesses of the manuscript, the ability of the research to be replicated, and the contribution of the work to the discipline. This study investigates the range of reviewer’s roles and responsibilities in relation to author’s own assessment as a frequent reviewer for fourteen journals representing five publishing houses—and as an active researcher—in comparison with a 2019 comprehensive study of the views of 224 authors on peer review. Based on this investigation, advice will be provided to potential reviewers regarding what is expected of them in undertaking their work. Recommendations will be offered for peer review to mitigate weaknesses in the process and increase the pool of qualified peer reviewers.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.480
GPT teacher head0.417
Teacher spread0.063 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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