Converting in the high performance pathway: An initial study of three year conversions
Bibliographic record
Abstract
One of the issues in development concerns the reliability of talent identification at young ages in terms of future success (conversion) as a national team athlete. In Canadian sport, the Long Term Athlete Development (LTAD) model (Balyi & Hamilton, 2004) has provided guidance concerning transition stages for youth athletes. Over the past ten years, the Canadian Sport Institute Pacific has been compiling lists of athletes who have been (i.e., identified as future National team potential). Data from PSOs (Provincial Sport Organizations) were collected across three athlete levels (Provincial Development Levels 1 and 2 and Canadian Development) from 2008-2013. Data was analyzed over a three-year window to determine successful conversion to the next level within this time band. Over 7000 athletes had been targeted across 46 sports (M years targeted = 2.43 yr). Conversion rates ranged between 20-27%, with more successful conversions at younger ages (vs 18 yrs). Although these data only provide information relating to rate (and indirectly, probability) of conversion, they provide a starting point to look at factors which led to successful conversion (such as the environments, competition success, other markers of athlete ability, age of identification) and to determine whether targeted pathways are most conducive to success. They also warrant discussion about the length of time that athletes can develop under high quality practice conditions and whether athletes are afforded a sufficient amount of quality practice in order to achieve expertise in their given sport.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".