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Record W2143675014 · doi:10.1111/oik.02956

Will technology trample peer review in ecology? Ongoing issues and potential solutions

2015· article· en· W2143675014 on OpenAlex
Pedro R. Peres‐Neto

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

VenueOikos · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPublicationProcess (computing)Computer scienceThe InternetField (mathematics)Peer reviewData sharingEngineering ethicsData scienceEcologySociologyPolitical scienceWorld Wide WebEngineeringLawBiology

Abstract

fetched live from OpenAlex

The classical view of peer review is that it is our primary process for assessing and judging whether research results should be published in a scholarly journal. However, the increased pressure to publish and technological developments are transforming peer review such that it is becoming a system that judges where work is published rather than whether the research is publishable (a ‘where rather than if’ process). Ecology is a field in which publication numbers puts a particular pressure on the review system. In this forum piece, I summarize the issues with the current publication system and discuss how technology is changing it, while suggesting solutions for important prior and ongoing issues with the peer review system. The view explored here is that technological developments (e.g. ease of creating journals, internet sites, storage, data generation, sharing of data and analytical code) will not eliminate peer review per se but will allow for a new set of parameters in which ethics and the optimal use of public funding will play a vital role in the evolution of the review process. Synthesis The number of papers and journals in Ecology has increased dramatically in the past decade. I present a critical overview of our review system and proposes that pressure to publish and technological developments have transformed peer review into a system that decides “where rather than if” papers are publishable. While reviewing the current pressures and factors playing a vital role in the evolution of the review and publication systems, I propose potential solutions to deal with current and future challenges to the peer review and publication systems.

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: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearchScholarly communication
Domain: Evaluation · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.047
GPT teacher head0.291
Teacher spread0.244 · 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