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Record W4379230918 · doi:10.3390/publications11020032

Roles and Responsibilities for Peer Reviewers of International Journals

2023· article· en· W4379230918 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

VenuePublications · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPublishingPeer reviewConsistency (knowledge bases)Technical peer reviewReading (process)Peer feedbackPublic relationsAdvice (programming)Narrative reviewMedical educationNarrativePsychologyPolitical scienceComputer scienceMedicineLaw

Abstract

fetched live from OpenAlex

There is a noticeable paucity of recently published research on the roles and responsibilities of peer reviewers for international journals. Concurrently, the pool of these peer reviewers is decreasing. Using a narrative research method developed by the author, this study questioned these roles and responsibilities through the author’s assessment in reviewing for five publishing houses July–December 2022, in comparison with two recent studies regarding peer review, and the guidelines of the five publishing houses. What should be most important in peer review is found discrepant among the author, those judging peer review in these publications, and the five publishing houses. Furthermore, efforts to increase the pool of peer reviewers are identified as ineffective because they focus on the reviewer qua reviewer, rather than on their primary role as researchers. To improve consistency, authors have regularly called for peer review training. Yet, this advice neglects to recognize the efforts of journals in making their particular requirements for peer review clear, comprehensive and readily accessible. Consequently, rather than peer reviewers being trained and rewarded as peer reviewers, journals are advised to make peer review a requirement for research publication, and their guidelines necessary reading and advice to follow for 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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Evaluation · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
gptMetaresearch
Domain: Evaluation · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.032
metaresearch head score (Gemma)0.230
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.230
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0340.067
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.801
GPT teacher head0.669
Teacher spread0.132 · 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