Misogynoir in women’s sport media: race, nation, and diaspora in the representation of Naomi Osaka
Bibliographic record
Abstract
Overt and subtle misogynoir (anti-Black misogyny) pervade sport and sport media, as women in the Black diaspora are rarely in control of sporting regulations or their media representations. One recourse racialized athletes have at their disposal, however, is active resistance. This paper provides a textual analysis of the intolerable misogynoir aimed at tennis professional Naomi Osaka, and key moments in her media (mis)representations. Results revealed three main themes: (1) ongoing misogynoir and colorism of sport media and athlete sponsors; (2) racial, national and diaspora media (mis)representations; and (3) resistance to gendered racism through self-representation. After Osaka’s historic win at the 2018 US Open, narratives of her Japanese nationality and Asian identity became the story that rendered her Blackness invisible, and enabled her to be read against her opponent Serena Williams. Some information and communication technologies (ICTs), including social media, presented counter-narratives and a recognition of the mainstream media vilification and erasure of Black women. At times, ICTs disrupted racist dominant narratives, and counter-narratives of Osaka’s Blackness and position as part of the Haitian jaspora (diaspora) prevailed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".