Champions, a Celebrity Crossover, and a Capitulator: The Construction of Gender in Broadsheet Newspapers’ Narratives About Selected Competitors at Wimbledon
Bibliographic record
Abstract
This study compared the narratives of 3 broadsheet newspapers of selected female and male tennis players competing in the Wimbledon Championships. From Canada, The Globe and Mail ; from Great Britain, The Times ; and from the United States, The New York Times were examined. Dominant narratives were identified from 161 articles taken from 44 newspaper editions during the 16-day period coinciding with the Wimbledon Championships fortnight. Drawing on Connell’s (1987, 1993, 2005) theory of gender power relations, textual analysis was used to examine the gendered narratives and, where it was applicable, how the gendered narratives intersected with race, age, and nationality. The results revealed that although the gendered narratives were at times complex and contradictory, they were generally consistent with dominant cultural patriarchal ideology and served to reiterate and legitimize the gender order.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".