Talent inclusion and genetic testing in sport: A practitioner’s guide
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
Current scientific evidence does not support the implementation of genetic tests to enhance the processes of talent identification and development systems. Regardless of this consensus, it appears likely that sport stakeholders will continue using genetic tests. This paper aimed to provide practitioners with some best practice guidelines if implementing genetic testing within their organisations. First, we assess the growth and perceived flaws of direct-to-consumer genetic testing companies targeted towards sport. The sports genomic literature is then summarised to demonstrate the lack of established genetic associations with sporting phenotypes and the prevalent limitations that exist in this field of research. Following this, examples are presented suggesting some stakeholders in sport have already used genetic tests to screen for variants associated with performance phenotypes, while the potential appeal of genetic information to sport stakeholders is also discussed. The value of increased genetic literacy (i.e., enhanced education/understanding of genetic information) is then considered, as well as the promotion of talent inclusion (i.e., using genetic tests to include or retain athletes rather than for de-selection and exclusion purposes). To conclude, we offer practitioners several recommendations and best practice guidelines with regards to the implementation of genetic testing in sport.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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