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Record W4225832830 · doi:10.1123/jsm.2021-0229

Addressing Gender Inequity in Sport Through Women’s Invisible Labor

2022· article· en· W4225832830 on OpenAlex

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

VenueJournal of Sport Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIdeologySociologyMacro levelMicro levelGender relationsGender studiesPublic relationsGender analysisPoliticsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

While the progress of women in the sport industry has become more visible, there is still significant gender inequity. Extending the sport organizational literature, we argue that the unpaid, invisible, and emotional labor of women, especially those holding diverse social identities, is significantly contributing to gender inequity at the organizational level. In broader sport research, the micro, everyday experiences of women stakeholders and the connection to macro societal structures and ideologies have provided foundational insight to build upon. However, there is a need for research to focus on the meso-level organizational practices, policies, designs, structures, and culture to create real change. Therefore, we present a conceptual paper, focused on a meso-level analysis and the invisible labors that women stakeholders engage in, to extend existing work and provide a pathway for further investigation into gender inequity in sport.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.348
Teacher spread0.265 · 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