Talent Identification and Development in Paralympic Contexts: Current Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This short review explores the state of talent identification and development of athletes in Paralympic contexts. While talent identification typically occurs during adolescence, this practice is more complex and variable in Paralympic contexts compared to non-Paralympic contexts. For example, Paralympic athletes can have impairments that are congenital or acquired at any time across their lives. Therefore, they can enter performance pathways at unpredictable times. Furthermore, differences and nuances associated with athlete impairments (type and severity), compounded by other systematic complexities (e.g., systems of classification) highlight the need to consider alternative and creative approaches to talent identification and development. We provide an overview of some of these complexities, highlight areas for future research, and provide recommendations for practitioners.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it