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Record W2037081747 · doi:10.1371/journal.pone.0092896

Imbalance in Individual Researcher's Peer Review Activities Quantified for Four British Ecological Society Journals, 2003-2010

2014· article· en· W2037081747 on OpenAlexaff
Owen L. Petchey, Jeremy W. Fox, Lindsay Haddon

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Calgary
FundersUniversität ZürichRoyal Society
KeywordsPeer reviewEcologyBiology

Abstract

fetched live from OpenAlex

Researchers contribute to the scientific peer review system by providing reviews, and "withdraw" from it by submitting manuscripts that are subsequently reviewed. So far as we are aware, there has been no quantification of the balance of individual's contributions and withdrawals. We compared the number of reviews provided by individual researchers (i.e., their contribution) to the number required by their submissions (i.e. their withdrawals) in a large and anonymised database provided by the British Ecological Society. The database covered the Journal of Ecology, Journal of Animal Ecology, Journal of Applied Ecology, and Functional Ecology from 2003-2010. The majority of researchers (64%) did not have balanced contributions and withdrawals. Depending on assumptions, 12% to 44% contributed more than twice as much as required; 20% to 52% contributed less than half as much as required. Balance, or lack thereof, varied little in relation to the number of years a researcher had been active (reviewing or submitting). Researchers who contributed less than required did not lack the opportunity to review. Researchers who submitted more were more likely to accept invitations to review. These finding suggest overall that peer review of the four analysed journals is not in crisis, but only due to the favourable balance of over- and under-contributing researchers. These findings are limited to the four journals analysed, and therefore cannot include researcher's other peer review activities, which if included might change the proportions reported. Relatively low effort was required to assemble, check, and analyse the data. Broader analyses of individual researcher's peer review activities would contribute to greater quality, efficiency, and fairness in the peer review system.

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)0.090
metaresearch head score (Gemma)0.247
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0900.247
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.068
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0000.001
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 head0.847
GPT teacher head0.566
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2014
Admission routes1
Has abstractyes

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