What Defines Early Specialization: A Systematic Review of Literature
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
Introduction While practitioners and organizations advise against early specialization, the lack of a consistent and clear definition of early specialization reduces the impact of recommendations and policies in youth sport. An important first step in understanding the consequences of early specialization is establishing what early specialization is. Objectives This PRISMA-guided systematic review aimed to determine the types, characteristics, and general content of early specialization papers within the literature, and examine how early specialization has been defined and measured in order to advance knowledge towards a clear and consistent definition of early specialization. Data sources Four different electronic databases were searched (SPORTDiscus, Web of Science, Sports Medicine and Education Index, and Scopus). Both non data-driven and data-driven studies were included to ensure a comprehensive understanding of the literature. Eligibility Criteria In order to be included in the review, the paper must: (a) Focus on specialization and explicitly use the term ‘specialization’(b) Focus on sport and athletes (c) Be papers from a peer-reviewed (d) Be in English. And finally, (e) be available in full text. Results 1371 articles were screened resulting in 129 articles included in the review after applying inclusion/exclusion criteria. Results indicated a clear discrepancy between key components of early specialization and the approaches used to classify early specializers. Conclusion Future research should work towards developing a valid and reliable approach to classifying early specializers and establishing a consistent definition across studies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.000 |
| 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