Book Review of Worley, Kristen and Joanna Schneller. (2019). Woman Enough: How a Boy became a Woman and Changed the World of Sport. Toronto: Random House Canada
Bibliographic record
Abstract
Kristen Worley and Johanna Schneller deliver a poignant story that unfolds as a compelling journey of self-discovery, resilience and advocacy.Worley begins her story as Chris, who grows up in a cold, conservative family where he always feels like an outsider.Early on, Chris becomes obsessed with the world of sports and finds refuge in water skiing and, eventually, bicycling.His sports helped him through turbulent times of mental health issues that he later learnt are related to his gender identity.In his 20s, Chris becomes Kristen and continues loving sports as a woman.Kristen becomes an advocate for trans rights when facing adversity while trying to compete in the Olympics.She filed a complaint against the sporting bodies that were keeping her from competing as a woman, and after years of fighting, she won the battle.This story is inspiring and truly important for learning and understanding trans-individuals' experience in sports.Worley masterfully captures the feelings of estrangement within a family system.From age three or four, Chris (Kristen Worley) already felt different from his family members.He could not succumb to the conservative family norms inflicted by Chris' father, Jim.With a traditional view of family life, the Jackson household was a strong reinforcer of gender roles, creating tension and 132
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.038 |
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".