Sports as education: Is this a stereotype too? A national research on the relationship between sports practice, bullying, racism and stereotypes among Italian students
Bibliographic record
Abstract
This article is based on scientific evidence from a national survey carried out in Italy in 2017 on a sample of 4011 students. The results of the statistical analysis show that the potential educational role of sports is not an explicit value embedded in its practice. In these terms, today the causal link between sports and education appears to be a stereotype. The study shows that teenagers who play sports outside of school have an increase in their levels of tolerance of bullying and racism. In addition, respondents who play sports have highly stereotyped opinions about gender roles and ethnic diversity. The neutrality of sports practice in Italy, with regard to social inclusion and the dissemination of positive values, has been demonstrated. Although sport can be a useful educational tool to mitigate limits arising from disadvantaged social conditions, a direct relation between sports and education has not been observed. In order to spread positive social values and promote social inclusion through sport, we hypothesise that it is necessary to overcome two limits: the inequality in sports opportunities among students and the weakness of the relation between sports and pedagogy. This article finally proposes a pedagogical approach aimed at sports teaching oriented towards social inclusion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".