Bibliographic record
Abstract
The overarching purpose of this dissertation was to explore the nature and extent of public shaming of professional athletes on social media in response to perceived legal, social, and sport-specific norm violations and the potential reasons that fans engage with athletes in this manner on social media. The methods employed were two-fold: for studies one and two, a qualitative content analysis of 7,700 fan comments on Facebook, Twitter, and Instagram directed at eleven male and female professional athletes from six different sports was conducted. To analyse these data, I implemented methodological pluralism by engaging in a semantic and subsequent latent thematic content analyses. For study three, guided by a narrative methodology, I conducted five semi-structured in-depth interviews with sport fans (three males, two females) who engaged regularly with professional athletes and teams on Twitter, Facebook, and Instagram. Findings cumulatively revealed that fans engage in public shaming of male and female professional athletes in response to the athletes’ perceived legal, social, and sport-specific violations. Public shaming on social media was evident with each athlete violation I examined, regardless of sport or experience level, and occurred across all platforms (Twitter, Facebook, and Instagram). Acts of shaming were illustrated by fans’ explicit withdrawals of support and descriptions of desired physical, psychosocial and career-related consequences for the athletes. In addition, there were undercurrents of gender and sexism observed across fans’ public shaming, which were exemplified through fans’ objectification of females, promotion of hyper-masculinity, and victim blaming. Fans proposed that public shaming acts occur in response to the threats that norm violations pose to fans’ sense of identification and belonging that have been cultivated through fandom and enhanced through the relationships perceived to be developed with athletes via social media. Specifically, fans suggested public shaming might be provoked when the integrity of fan-athlete relationships are challenged by legal, social, or sport-specific norm violations. Based on the collective findings, I explained the theoretical, methodological, and applied contributions of this dissertation to the existing literature, proposed ethical considerations for research of this nature, and discussed recommendations for future directions in this area of scholarship.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".