“Her Winning Personality”: Critical Discourse Analysis of Media Portrayals of Females in Sport
Bibliographic record
Abstract
At the intersection of fourth-wave feminism and third-wave sports media research, this critical discourse analysis will focus on the ways in which gender hierarchy and gender expectations are manifested in articles on ESPN.com. Through the investigation of sports media framing techniques, the ESPN articles in examination construct an idealized female identity within sports through the language used. This narrow view of female athletes allows for the power and influence that sports media has to construct gender hierarchies in the media landscape. Using Fairclough’s (1989) method of conducting a critical discourse analysis, the prevalent sports media sentiments about Simone Biles, Megan Rapinoe, and Serena Williams will illustrate the sexist, racist, and homophobic language used. Through applying the Televised Sports Manhood Formula (Messner et. al, 2000) as a foundational discourse in sports media to journalism, the hierarchy of sports media results in the use of character framing techniques for sportswomen. When aspects like ambivalence and non-sports related information are emphasized, these strategies uphold the masculine hegemony of sports media. Keywords: Sports media sentiment, gender, gender hierarchy, critical discourse analysis, Simone Biles, Megan Rapinoe, Serena Williams.
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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".