Ethics, bioethics and physical education: a systematic review of a necessary convergence
Bibliographic record
Abstract
Abstract This article seeks to identify what scientific journals have been publishing in terms of ethics and bioethics in the field of physical education. We carried out a systematic literature review in January 2020 in LILACS, SciELO, Web of Science, Virtual Health Library, PubMed, Scopus, ERIC, and SPORTDiscus. Articles in English published between 2005 and 2018 were found, with 12 studies selected according to the expected inclusion criteria. It resulted in four categories: physical education as a means for personal development in the school environment, potential of sport as a means for moral education as virtue formation, level of knowledge of physical education professionals about ethics, and ethics in the training of these professionals. We consider that, although issues related to ethics and bioethics in physical education appear in the literature, expanding studies on this theme, still on the margins of scientific production in the field, is necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".