Advancing women’s cycling through digital activism: a feminist critical discourse analysis
Bibliographic record
Abstract
Research question Social media in sport management contexts is increasingly used to highlight social issues in sport and to advocate for change, such as expanding the opportunities for women to participate. The purpose of this study is to examine how and why people strategically used various Twitter conventions to advocate for women’s cycling during the 2013 (men’s) Tour de France. We draw on Feminist Critical Discourse Analysis to frame our exploration and analysis of the issue.Research methods We analyzed the text of approximately 6000 tweets to examine the use of Twitter conventions, as discursive practices, in digital activism efforts to advance the women's cycling agenda.Findings and discussion People used links, retweets, hashtags, direct mentions, and influencers’ posts as individual discursive practices and for their collective potential to draw attention to, and advocate for, women’s pro-cycling in the context of the 100th iteration of the men’s Tour de France. We discuss why this was an important process in the context of women’s cycling, and some of the impacts, ten years later, of this Twitter activity.Implications Twitter conventions can be a useful digital activism tool for feminist agendas in sport. We are cautious of overstating this case as each cause will have different contexts, and the ability of trolls and other users to derail activism is ever present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".