Thinking Ethically About Professional Practice in Adapted Physical Activity
Bibliographic record
Abstract
There has been little critical exploration of the ethical issues that arise in professional practice common to adapted physical activity. We cannot avoid moral issues as we inevitably will act in ways that will negatively affect the well-being of others. We will make choices, which in our efforts to support others, may hurt by violating dignity or infringing on rights. The aim of this paper is to open a dialogue on what constitutes ethical practice in adapted physical activity. Ethical theories including principlism, virtue ethics, ethics of care, and relational ethics provide a platform for addressing questions of right and good and wrong and bad in the field of adapted physical activity. Unpacking of stories of professional practice (including sacred, secret, and cover stories) against the lived experiences of persons experiencing disability will create a knowledge landscape in adapted physical activity that is sensitive to ethical reflection.
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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 | 0.081 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.122 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 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".