Les 16 jours à Pékin de Chantal Petitclerc : discussion autour de la constitution d’une contre-mémoire de paralympienne
Bibliographic record
Abstract
Former Canadian Paralympian Danielle Peers (2009) shows how the dominant historical narrative of the Paralympic Movement renders disabled athletes anonymous while portraying them as being passive and tragic. To study in more detail the perspective of an athlete, we analyze the book 16 jours à Pékin (literally, “16 days in Beijing”) by Canadian Paralympic champion Chantal Petitclerc. Part autobiography, part travelogue, this book offers a different understanding of Paralympians by acting as a memory technology, i.e., both as a specific mode of archiving and as a narrative technique. Petitclerc presents herself as an elite athlete and as a woman, lover, and friend, in a way that challenges the dominant Paralympic narrative of anonymous and de-individualized athletes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".