Talent inclusion: An imperfect solution to genetic testing in sport - Response to commentaries
Bibliographic record
Abstract
We are extremely grateful our esteemed colleagues Craig Pickering, Duarte Araújo, Keith Davids, and Kevin Till have read and offered insightful reflections on the target article “Talent inclusion and genetic testing in sport: A practitioner’s guide”. We take the opportunity in the present article to respond to the three commentaries provided by these authors. In our target article, we highlighted at this moment in time, there is unequivocal disapproval in the scientific community with regards to the implementation of genetic testing in sport. Despite an insufficient evidence base, however, various stakeholders (e.g., athletes, support staff) have used, and will likely continue using, genetic tests. We offered potential explanations regarding the allure of genetic information to sports stakeholders before suggesting some imperfect solutions in terms of increasing genetic literacy, promoting talent inclusion, and following a minimum set of best practice guidelines.
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 | 0.041 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.035 | 0.070 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".