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Record W2531842628 · doi:10.1186/s41073-016-0022-7

Is it becoming harder to secure reviewers for peer review? A test with data from five ecology journals

2016· article· en· W2531842628 on OpenAlexaff
Arianne Albert, Jennifer Gow, Alison Cobra, Tim Vines

Bibliographic record

VenueResearch Integrity and Peer Review · 2016
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsWorld Wildlife Fund CanadaUniversity of British ColumbiaWomen's Health Research Institute
Fundersnot available
KeywordsEcologyCommunityPublishingFunctional ecologyEvolutionary ecologyRhetorical questionBiologyHabitatPolitical scienceEcosystemLaw

Abstract

fetched live from OpenAlex

There is concern in the academic publishing community that it is becoming more difficult to secure reviews for peer-reviewed manuscripts, but much of this concern stems from anecdotal and rhetorical evidence. We examined the proportion of review requests that led to a completed review over a 6-year period (2009–2015) in a mid-tier biology journal ( Molecular Ecology ). We also re-analyzed previously published data from four other mid-tier ecology journals ( Functional Ecology , Journal of Ecology , Journal of Animal Ecology , and Journal of Applied Ecology ), looking at the same proportion over the period 2003 to 2010. The data from Molecular Ecology showed no significant decrease through time in the proportion of requests that led to a review (proportion in 2009 = 0.47 (95 % CI = 0.43 to 0.52), proportion in 2015 = 0.44 (95 % CI = 0.40 to 0.48)). This proportion did decrease for three of the other ecology journals (changes in proportions from 2003 to 2010 = −0.10, −0.18, and −0.09), while the proportion for the fourth ( Functional Ecology ) stayed roughly constant (change in proportion = −0.04). Overall, our data suggest that reviewer agreement rates have probably declined slightly but not to the extent suggested by the anecdotal and rhetorical evidence.

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
gemmaMetaresearchBibliometrics
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
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)0.322
metaresearch head score (Gemma)0.733
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.836

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3220.733
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0110.020
Science and technology studies0.0030.007
Scholarly communication0.0060.013
Open science0.0050.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.922
GPT teacher head0.710
Teacher spread0.212 · 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.

MetaresearchBibliometrics

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

Study designObservational
DomainEvaluation
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

Citations28
Published2016
Admission routes1
Has abstractyes

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