Mobilising gender equality: A discourse analysis of bids to host the FIFA Women’s World Cup 2023™
Bibliographic record
Abstract
After an exciting bidding process featuring competing submissions from Brazil, Colombia and Japan, Australia and New Zealand were chosen to co-host the FIFA Women’s World Cup 2023™. Using feminist discourse analysis to examine the narrative strategies employed by the bidding nations, this article demonstrates that bidding nations discursively mobilised themes of gender equality to position their bids favourably. They did so by asserting themselves as leaders in women’s sport and gender equality, and by emphasising strategies for growing women’s football. Bidding nations situate themselves as benevolent rescuers of struggling women’s sport without acknowledging their accountability for policies and practices that disenfranchised women’s football in the first place. This article argues that the mobilisation of gender equality discourses by bidding nations problematically uses neoliberal feminist logics, stripping pro-women messages such as equal opportunity and empowerment of political context and repackaging them in commercially viable ways. Ultimately, although bidding nations use discourses of gender equality to position themselves favourably, existing levels of gender inequality reveal the limits of their positioning.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".