MétaCan
Menu
Back to cohort
Record W4379230918 · doi:10.3390/publications11020032

Roles and Responsibilities for Peer Reviewers of International Journals

2023· article· en· W4379230918 on OpenAlexaff
Carol Nash

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.

How this classification was reachedexpand

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.175
metaresearch head score (Gemma)0.469
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1750.469
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.006
Science and technology studies0.0150.008
Scholarly communication0.0290.011
Open science0.0040.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.006

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

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual · Not applicable
DomainEvaluation
GenreMethods · Commentary

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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePublicationsSame topicscientometrics and bibliometrics researchCategoryMetaresearchFrench-language works237,207