Investigating the Interdependence of Strain and Self-Control
Bibliographic record
Abstract
Self-control and strain perspectives are widely viewed as independent and contrasting explanations for crime and delinquency. This paper re-evaluates the competing paradigms approach by considering the two theories as potentially complementary in explaining participation in delinquency based on Gottfredson and Hirschi's (1990) assumption that self-control acts as a barrier to criminal behaviour. If such a claim is valid, one would hypothesize that individuals with high self-control would be able to mediate the effects of strain and refrain from engaging in delinquent activities. In contrast, adolescents with low self-control may not be equipped with the necessary constraints to abstain from delinquency and would therefore exhibit the greatest criminal propensities. A significant interaction term would support such claims. Data from a sample of over 2,000 adolescents attending junior and senior high schools in a western Canadian city were analysed to determine the independent and contextual effects of self-control and strain on involvement in delinquent behaviour. Results suggest that both self-control and strain are important contributors to delinquency, but in an additive and not an interactive way. Such results do not seem to provide support for claims made by control theorists, who would no doubt argue that the effects of strain should be conditioned by low self-control.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".