Introduction: The Binary World of Sport: Belonging and Resistance
Bibliographic record
Abstract
Abstract In the face of widespread opposition and hostility, trans and nonbinary athletes, from recreational to professional levels, continue to resist exclusion and oppression by daring to compete, participate and play. The long-standing binary thinking that characterizes sport poses particular challenges for trans women, who are positioned by advocates of trans exclusion as an alleged threat to women's sport. As context for this discussion, Lenskyj examines how social psychologists have contributed to understandings of belonging and community and the implications for trans and nonbinary athletes' rights to share the benefits that sport offers. The concept of ‘deliberative freedoms’ – including freedom to live one's life without having others view certain traits as ‘costs’ – provides a framework for investigating resistance. Greey then draws on a sociological understanding of gender to argue that inclusion is not synonymous with belonging. Belonging for trans athletes, Greey argues, requires more than the ‘letter of the law.’ Belonging requires recognition from teammates, coaches and other sport community members. An overview of terminology is presented, followed by an overview of chapters, summarizing the key themes and findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".