Bibliographic record
Abstract
Analyzed through the lens of social-bond theory, this thesis examines the relationship between sports participation and delinquency among adolescents. The purpose of this thesis is to better understand whether sports can serve as an effective intervention strategy for policy makers, government agencies and criminal justice branches that deal directly with at risk-youth or offenders who can benefit from sports-related programs. Through the use of a meta-analysis methodological design, the findings uncovered through common literature will reflect the extent to which social-bond theory can sufficiently explain delinquency among athletes. Traditionally, sports-participation and physical activity have been connected to prosocial stereotypes and the belief that adolescents will develop character-building morals. Although many situations including sports-participation are mainly positive across most facets, there is further evidence to suggest that unintended, antisocial-developing consequences can arise from participation in sports-related activities. Jock identity and unstructured socializing were highlighted as major factors for delinquency among athletes, whereas the pedagogical sports-environment serves as a possible deterrence model for delinquency. With further extensive research into this topic, the development of a pedagogical sports model can provide more athletes with an exceptional prosocial experience. Similarly, sports participation and sports-related intervention strategies can be utilized to address and combat youth-crime.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".