Interpreting and Implementing the Long Term Athlete Development Model: English Swimming Coaches' Views on the (Swimming) LTAD in Practice
Bibliographic record
Abstract
INTRODUCTION The article by Melanie Lang and Richard Light provides interesting information related to the difficult task of adapting a swimming training programme to the general guidelines that the sport governing bodies impose to obtain funding support for the competitive programme. In general terms, a long term athlete development (L TAD) model is written by sport experts to define a general pathway of athlete development to achieve national or international performances in dif ferent sports or in a particular sport based on an interdisciplinary scientific knowledge. Numerous such models have been published and distributed for years in many countries and seek to guide the achievement of outstanding performances in many sports (see Canadian and English examples [1, 2]). The programme application entails a considerable ef fort on the part of the participants (swimmers, coaches and clubs), but unfortunately this ef fort seems absolutely necessary to obtain international performances. The problem arises when external guidelines based on LTAD may contradict, in some cases, the competitive rules that should stimulate participation in this long-term programme, as the cited article tries to demonstrate. However, I wish to deal with the conclusion of the article that concerned the impact of excessive volume upon development of technique. This led me to deal with the lack of attention that swimming-planning specialists devote to this highly influential factor in swimming performance.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".