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Record W4404567963 · doi:10.1177/08902070241301629

Examining personality psychology to unpack the peer review system: Towards a more diverse, inclusive, and equitable psychological science

2024· article· en· W4404567963 on OpenAlexaff
Olivia E. Atherton, Dulce Wilkinson Westberg, Vernita Perkins, Katherine M. Lawson, Alma Jeftić, Eranda Jayawickreme, Stella Zhang, Z.J. Hu, Kate C. McLean, Julia G. Bottesini, Moin Syed, Joanne M. Chung

Bibliographic record

VenueEuropean Journal of Personality · 2024
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyPsychological sciencePersonalitySocial psychologyApplied psychology

Abstract

fetched live from OpenAlex

Peer review serves to evaluate the scientific validity and quality of research by other researchers within the same field. Psychology, like many other disciplines, uses peer review to determine whether researchers’ work is published, where it is published, and in what form. However, the peer review system is imperfect, often perpetuating harm, exclusion, and inequities. We believe these problems hinder psychology from becoming a truly representative and valid science. In this paper, we uncover the historical roots of the peer review system in psychology and describe how these roots persist today. Then, using personality psychology as an exemplar, we leverage a social justice lens to: (1) document key contemporary problems in the peer review system; (2) identify global challenges in peer review; and (3) provide recommendations and guiding questions that might help the field become more equitable, diverse, just, and inclusive. To do so, we draw upon three sources of information: our personal experiences, qualitative and quantitative data from 104 self-identifying personality psychologists, and the prior literature. Ultimately, the goal of this work is to give voice to those who have been harmed in peer review, and to inform how we might reimagine peer review in personality psychology and beyond.

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)0.202
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2020.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.059
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0040.002
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 head0.684
GPT teacher head0.618
Teacher spread0.066 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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