Bibliographic record
Abstract
Our target article on ‘Innate talent’ had two objectives, first to acknowledge the 20th anniversary of the seminal contribution by Howe, Davidson and Sloboda (1998) and second, to update this information as it relates to talent in the domain of sport. Many thanks to all the authors that took the time to provide commentaries on our review. Broadly, our target paper focused on 1) whether the concept of innate talent was reasonable and scientifically sound and 2) whether the concept of innate talent had any utility to those working at the coalface of sport science (e.g., coaches, scouts, etc.). All of the commentaries were complimentary to our review, which suggested continued interest in this area (although this was noted as surprising by Hambrick and Burgoyne). We have tried to respond to all of the interesting points raised by the commentaries, but this was not always possible. That said, we grouped our responses under general themes below. Our impression, based on the commentaries, is that innate talent is not a contested concept; in that there appears to be agreement (for the most part) that, ‘this thing exists’. Rather, the concept of innate talent is contestable (Gallie, 1956); that is, there is debate about exactly what it is, the degree of its influence, and how useful the concept of innate talent is.
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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 | 0.042 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".