The power of the talent category. A social-relational perspective on the identification and selection of young football players
Bibliographic record
Abstract
Objectives: Football associations in a number of western nations, including Denmark and Germany, are initiating nationwide programs to identify and select talented football boys already at the age of 10-12. The aim of this article is to explore how boys this age experience such early talent identification and selection processes. Methods: Using an ethnographic approach, the first author conducted 1.5 years of fieldwork in a Danish suburban football club from which several boys were selected and even more boys not-selected to an extracurricular nation-wide program. In the club, the first author observed and participated in weekly training practices of all U10/11 boys and also conducted six focus-group interviews with 19 of the boys. Findings and Discussion: The material was analyzed with Richard Jenkins’ perspectives on social identity, and specifically his distinction between external categorization and internal identification. Observations from weekly training practices displayed that level of skills and talent made up a very predominant category through which the coaches grouped the boys, despite the boys tried to relate in a variety of ways e.g. as school mates and friends. Notwithstanding, in the focus group interviews talent and skills also constituted the main reference for the boys’ internal identification of themselves and their peers. Conclusion: The analysis shows that the football system contributes to reduce the variety of grouping options among young boys. If individual boys succeed in negotiating their identities they do so in pointing to specificities in skills and talent that they are the sole owners of.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".