Bibliographic record
Abstract
In an earlier issue of the International Review for the Sociology of Sport, Eugen König argued that doping exposes sport as an enterprise which is inherently exploitative (1995). Doping is consistent with other practices and technologies which push human limits of performance but which are arbitrarily included as part of `pure', `natural', and `authentic' sport only because they are not against the rules. By circumscribing what is to count as ethical inquiry in sport within a discussion of obligations in relation to proscriptive rules, sport ethicists cannot avoid being complicit in supporting a sport culture that is often harmful to athletes. König charges that a sport ethics that concerns itself only with questions that emanate from rule breakage `does not deserve the name of ethical criticism' and is `a powerless protest against sport' and `actually prevents what it pretends to intend' (1995: 256). We take König's critique of sport ethics seriously and, through this commentary, we aim to initiate a discussion about a new sport ethics that would have quite different pedagogical, political and scholarly tasks. The discussion is situated in the ethics of French intellectual, Michel Foucault, and contextualized in the proliferation of ethical concerns and debates within contemporary Canadian sports discourse.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".