A Cluster Analysis of Gender Discrimination in Chinese and Western News Media
Bibliographic record
Abstract
This study is a rhetorical analysis of the gendered language both Chinese and Western news media perpetuate in depicting the female Chinese Olympian Ye Shiwen at the 2012 London Olympics. The analysis reveals that both media employ a language that constructs Ye as an immature, childlike being whose achievements are not only unexpected but unnatural. After initially identifying Ye with the doping history of Chinese athletes in the 1990s, Western journalists depicted her as a passive victim of unethical child training programs, described in terms which imply a rhetorical identification with China’s doping history. In its turn, Chinese news media defended the integrity of its Olympic ethos by casting Ye as a dutiful daughter, and innocent child; this construction of her ethos absolves her of blame by denying her agency, effectively placing strict boundaries around her ownership of achievement, boundaries which reflect normalized assumptions of submissiveness as appropriate female behavior. The readiness of both the Western and Chinese media to default to a rhetoric of gender discrimination when norms are challenged demonstrates how the Olympic ideal of surpassing boundaries can still be a closed border for Chinese women.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".