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Record W4411054057 · doi:10.1123/jsep.2024-0252

Breaking Barriers: Female Officials’ Motivations and Experiences in Male-Dominated Officiating Environments

2025· article· en· W4411054057 on OpenAlexaff
Alice A. Theriault, David J. Hancock

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsThematic analysisPublic relationsPolitical scienceQualitative researchPsychologySociologySocial science

Abstract

fetched live from OpenAlex

In most sports, more male than female officials are recruited and retained. The limited research focusing on female sport officials suggests that their experiences are frequently negative. Further understanding of female sport officials' experiences is imperative for learning more about their intentions to begin and continue as officials. The purpose of this study was to explore the positive and negative experiences of female officials who operated in sports where the officials were primarily male. Nine sport officials participated in semistructured interviews. Thematic analysis was used to identify and code common themes within the data, many of which aligned with the principles of self-determination theory. The main themes discussed herein are (a) the female experience, (b) facilitators, and (c) barriers. Recommendations are provided, which might contribute to future policy changes that lead to increased recruitment and retention of female officials.

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 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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.004
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.016
GPT teacher head0.321
Teacher spread0.305 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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