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Record W3196835254 · doi:10.1126/sciadv.abe4639

The gendered nature of authorship

2021· article· en· W3196835254 on OpenAlex
Chaoqun Ni, Elise Smith, Haimiao Yuan, Vincent Larivière, Cassidy R. Sugimoto

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

VenueScience Advances · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsDevaluationAttributionPower (physics)InjusticeSociologyEconomic JusticePsychologySocial psychologyCurrencyPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

Authorship is the primary form of symbolic capital in science. Despite this, authorship is rife with injustice and malpractice, with women expressing concerns regarding the fair attribution of credit. Based on an international survey, we examine gendered practices in authorship communication, disagreement, and fairness. Our results demonstrate that women were more likely to experience authorship disagreements and experience them more often. Their contributions to research papers were more often devalued by both men and women. Women were more likely to discuss authorship with coauthors at the beginning of the project, whereas men were more likely to determine authorship unilaterally at the end. Women perceived that they received less credit than deserved, while men reported the opposite. This devaluation of women’s work in science creates cumulative disadvantages in scientific careers. Open discussion regarding power dynamics related to gender is necessary to develop more equitable distribution of credit for scientific labor.

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.

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)0.031
metaresearch head score (Gemma)0.119
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.119
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.241
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.492
GPT teacher head0.610
Teacher spread0.119 · 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