Where's the money? Incentives, coaching, and the long-term athlete development model.
Bibliographic record
Abstract
The purpose in this paper is to examine the effectiveness of the long-term athlete development (LTAD) model compared to a model based on economic incentives. The emphasis, however, is on the LTAD model and reasons why it is incompatible with coach education, particular the coaching of judo. The major influences on the LTAD approach are reviewed along with recent evidence that leads to questions about its usefulness. While Judo Canada has attempted to implement the LTAD model in its program to train coaches, there remains a great deal of incongruity between the LTAD and the pedagogy that often characterizes judo. As a result, coaches who follow a program of certification do not, subsequently, employ what they have learned but, rather, return to their ‘old ways’. I argue that the incentives for becoming certified are wrong. I conclude that, rather than the attempt to standardize coaching via the LTAD model is misguided because a system that facilitates innovation is desired. Financial incentives whereby coaches and athletes are amply rewarded for success provide a better route to innovation and the Olympic podium than long-term athlete development model.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".