How Tweet It Is: A Gendered Analysis of Professional Tennis Players’ Self-Presentation on Twitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The innovations of social media have altered the traditional methods of fan–athlete interaction while redefining how celebrity athletes practice their roles as celebrities. This study explored gender differences in professional athletes’ self-presentation on Twitter. Content analyses were used to compare male and female athletes’ tweets relayed by all professional tennis players with a verified Twitter account. Profile details and messages were scoured for themes and patterns of use during the time surrounding the 2011 U.S. Open Tennis Championships. Goffman’s seminal 1959 theory of self-presentation guided the analysis. While athlete image construction was found to be largely similar between genders, male athletes were found to spend more time in the role of sport fan while female athletes spent more time in the role of brand manager.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it