Bibliographic record
Abstract
Background Hirschi’s social control theory (SCT) posits that bonds to conventional society deter delinquency. According to SCT, adolescent involvement in prosocial extracurricular activities should reduce offending. This study extends on past research by examining how participation in sports, arts, and academic clubs predicts later violent and property crimes, while controlling theoretically relevant predictors.Methods Data from Waves I and II of the National Longitudinal Study of Adolescent to Adult Health (Add Health) were used (n = 2,443). Extracurricular involvement was measured using dichotomous and continuous (to assess dose-response relationships) measures, and a composite variable capturing combinations of activity types.Results Youth involved in sports reported more property and violent offenses. A dose-response pattern was found: the more sports clubs a student joined, the more likely they were to engage in violent behavior. Conversely, greater involvement in arts clubs was associated with fewer offenses. Academic club participation showed no significant association. Notably, students involved in all three activity types reported the highest levels of property crime.Conclusion Findings challenge the assumption that all extracurricular involvement protects against delinquency. While arts engagement was protective, sports, especially multiple club involvement, may increase risk, highlighting the need to consider activity type, intensity, and overlap.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".