Is it too early to condemn early sport specialisation?
Bibliographic record
Abstract
There has been a rapid and substantial increase in scholarly and public discourse regarding the perceived consequences of single sport specialisation during periods of early development. Since 2017, there have been three systematic reviews and 10 narrative reviews/editorials about the negative implications of specialisation in sport. A 2009 review in this area asked ‘what do we know about early sport specialisation’ and concluded ‘not much’.1 In this editorial, we argue things have not changed much in the intervening decade, despite the considerable increase in rhetoric around this subject. Much of the discussion in this area positions specialisation as binary, either you are specialising or you are not, despite clear evidence that the patterns of early engagement in youth sport are more diverse.2 Usually, discussions focus on ‘engagement in a single sport to the exclusion of all others’ without acknowledging the limits of this simple distinction. For instance, if there is a dose–response relationship between the quantity and/or type of exposure and likelihood of positive outcomes, does the number …
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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 | 0.024 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".