"Drop and give me 10": Re-thinking physical conditioining practices
Bibliographic record
Abstract
The purpose of this presentation is to examine the commonly implemented practice of physical conditioning. Needless to say, physical conditioning and pushing one's body physically is not only an inherent part of sport training but is a requirement for skill acquisition and performance enhancement. Moreover, it is the coach's role, in part, to implement physical conditioning programmes and to encourage athletes to push themselves out of their "comfort zone" physically and psychologically in order to become more fit or skilled. Apart from legitimate uses of physical conditioning for athlete development however, there are also examples of the use of physical conditioning for the purposes of punishment. Directing a team to engage in exhausting conditioning as punishment for poor performance or undesirable behaviors such as arriving late for practice or missing curfew are some examples. In such cases, physical conditioning is used for the sole purpose of punishment and is unrelated to the enhancement of an athlete's physical condition. It is the supposition of this presentation that under certain conditions the use of physical conditioning represents a form of physical and perhaps emotional harm. In this presentation, we will explore the conditions under which the coach's implementation of physical conditioning represents a form of abuse versus a legitimate method for performance enhancement and athlete development.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".