Bibliographic record
Abstract
Despite a large body of multidisciplinary research and several models, successful athlete development remains fraught with inefficiencies, and negative outcomes. The aim of this symposium is to highlight new approaches, as well as critical perspectives, on how to manage athlete development. The first two presentations discuss findings from a unique high performance student-athlete development program, the Academy for Student Athlete Development (ASAD). Knibbe et al describe the sport-school model of ASAD, and how qualitative data have been used to adapt the program over the first few years of its existence. Mosher and colleagues present quantitative data describing the impact of the ASAD on athlete-level education, social and athletic outcomes using pre and post-test data. The third and fourth presentations focus on the challenges of identifying and developing talent in sport. Using data from 46 targeted sports across three developmental levels in British Columbia, Hill et al quantify the probability of conversion across levels of sport for talented athletes, and critically discusses the utility of targeted pathways, and current notions of when athletes should be targeted for talent pathways. Schorer eschews predominant multivariate 'formula' approaches to talent identification in favour of a bounded rationality approach using simple heuristics, based on decision-making research. The symposium appropriately concludes with results of a systematic review from Lemez on end of athletic career transitions, which emphasizes the role of psychosocial and environmental factors that influence successful transitions. All presentations stress the need to challenge conventional approaches, for rigorous and critical research, to optimize athlete development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".