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Record W4405383870 · doi:10.1177/01968599241302849

Open Road to Competition: Media Framing of Social Justice Within Professional Women's Cycling

2024· article· en· W4405383870 on OpenAlexaff
Amy Rundio, Cory Kulczycki, Arshpreet Kaur Mallhi

Bibliographic record

VenueJournal of Communication Inquiry · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsFraming (construction)CyclingSocial justiceSociologyMedia studiesEconomic JusticePolitical scienceCriminologyLawGeography

Abstract

fetched live from OpenAlex

The creation of women's versions of popular men's cycling races sparked plenty of media attention as women had historically been denied access to these spaces. Since access to these races and subsequent media provides opportunities and benefits for cyclists, the races become potential sites of social justice, underscoring the importance of understanding the media narrative of social justice in women's cycling. Therefore, we analyzed the presence of the five propositions (distributive justice, procedural justice, interactive justice, recognition, care, and repair) of social justice in public spaces in media narratives surrounding the women's Paris-Roubaix and Tour de France. Issues of distributive, procedural, and interactive justice were present in arguments that gatekeepers could not invest in women's cycling without proof of success, as well as in gatekeepers’ reputation of intolerance. Women faced disproportionate burdens due to lack of resources, representing issues of recognition, procedural justice, and distributive justice.

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)0.005
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 score0.024
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0160.034
Scholarly communication0.0150.008
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.091
GPT teacher head0.420
Teacher spread0.329 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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