THE STATE OF WOMEN'S SPORTS ON THE WEB: CONTENT ANALYSES OF INTERNATIONAL SPORTS NEWS WEBSITES AND ATHLETES' TWITTER PROFILES
Bibliographic record
Abstract
With the rise of the Internet as a source of news, a deeper investigation into the gender issues related to the online coverage of sports is much needed. This is the purpose of this dissertation, fulfilled through four quantitative content analyses. The first study is a content analysis of the homepages of eight sports news websites from four western countries: the United States, Canada, France and Great Britain. Results from the multi-national indicated that despite the limitless function of the Internet, sports coverage remains overwhelmingly male. The second study uses data from the same sampled websites as the first one to get a closer look at the portrayal of female athletes in online sports news compared to the portrayal of male athletes. Results indicated that traditional gender stereotypes are still present in online sports coverage, and that men are given more prominence than women are. The third study turns to social media with a content analysis of athletes' Twitter profiles. The profile pictures, background pictures and Twitter biographies of athletes were sampled to examine how athletes frame themselves on Twitter. Results indicated that female athletes simultaneously preserve and counter traditional gender roles on their Twitter profiles. Female athletes tend to highlight their femininity visually through their pictures, but often focus on their athletic careers in their biographies. Finally, the fourth study is a comparative content analysis of pictures from sports news websites (taken from the second study) and Twitter profile pictures. Results indicated that websites were more likely to highlight a subject's athleticism than Twitter profile pictures were, regardless of the athlete's gender. However, differences were found to be wider for women than for men. By providing quantitative analyses about the state of coverage and exposure of women's sports on the Internet, as the medium's popularity continues to grow, this dissertation informs the ongoing debate about gender equality in the media and in the sports universe, especially when both worlds intertwine.
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 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 | 0.000 | 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 | 0.001 | 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".