Re-establishing the ‘outsiders’: English press coverage of the 2015 FIFA Women’s World Cup
Bibliographic record
Abstract
In 2015, the England Women’s national football team finished third at the Women’s World Cup in Canada. Alongside the establishment of the Women’s Super League in 2011, the success of the women’s team posed a striking contrast to the recent failures of the England men’s team and in doing so presented a timely opportunity to examine the negotiation of hegemonic discourses on gender, sport and football. Drawing upon an ‘established-outsider’ approach, this article examines how, in newspaper coverage of the England women’s team, gendered constructions revealed processes of alteration, assimilation and resistance. Rather than suggesting that ‘established’ discourses assume a normative connection between masculinity and football, the findings reveal how gendered ‘boundaries’ were both challenged and protected in newspaper coverage. Despite their success, the discursive positioning of the women’s team as ‘outsiders’, served to (re)establish men’s football as superior, culturally salient and ‘better’ than the women’s team/game. Accordingly, we contend that attempts to build and, in many instances, rediscover the history of women’s football can be used to challenge established cultural representations that draw exclusively from the history of the men’s game. In such instances, the 2015 Women’s World Cup provides a historical moment from which the women’s game can be relocated in a context of popular culture.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".