Street Youth, Unemployment, and Crime: Is It That Simple? Using General Strain Theory to Untangle the Relationship
Bibliographic record
Abstract
Researchers have called for greater attention to be paid to the variables linking unemployment to crime. In particular, it has been suggested that people's interpretation of their labour market situation plays a large role in shaping their responses to it. Utilizing general strain theory, this research examines the role that unemployment plays in the criminal behaviour of 400 homeless street youths. Of particular interest is the way that these youths interpret their labour market experiences and how together these interpretations and experiences influence criminal behaviour. Findings reveal that the effect of unemployment on crime is mediated and moderated primarily by other variables. In particular, unemployment is conditioned by external casual attributions that lead to anger over unemployment, which in turn leads to crime. The direct effect of unemployment on crime is moderated by monetary dissatisfaction and minimal employment searches. Anger over unemployment is also the result of negative subjective interpretations of economic situations and a continued attachment to the labour market. In addition, these negative subjective perceptions, the lack of state support, a decrease in social control, and prolonged homelessness lead to greater participation in criminal activities directly. Criminal involvement is also encouraged by peers, deviant values, and a lack of fear of punishment. Findings are discussed and suggestions for future research are offered.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".