The Dark Side of Social Support: Examining the Complex Relationship Between Social Support and Crime
Bibliographic record
Abstract
Cullen (1994) outlines that social support is a key concept in understanding criminal involvement. Prior research, however, has failed to examine the full range of support types that might impact offending. To date, work has focused primarily on expressive and instrumental forms of social support stemming from legitimate sources (family members and conventional peers) decreasing individuals’ offending likelihoods. Past examinations have overlooked how receiving legitimate social support from non-conventional sources (criminal peers) may influence illegal behaviour. Moreover, these investigations ignore the possibility that illegitimate social support from non-conventional resources might escalate offending propensities. Using a sample of 287 street youth in the Greater Toronto Area, the current study adds to the social support/crime research by incorporating multiple forms of legitimate social support emphasizing the importance of diverse sources (family and service providers) in discouraging criminal propensities. Further, it extends the literature by incorporating street peers and street families—often viewed as illegitimate sources—into the examination of legitimate social support. Finally, the study investigates how illegitimate social support from non-conventional sources encourages offending. Drawing on three theoretical perspectives: social support theory (Cullen 1994), differential coercion and social support theory (Colvin et al. 2002), and general strain theory (Agnew 1992), possible direct, mediating, and moderating impacts of social support forms on organized crime are explored. Additionally, the research considers the role of various adverse experiences, anger, and self-control to better specify the influence of the dissimilar types of social support on offending. Findings suggest that the relationship between social support and crime is nuanced. Illegitimate forms of social support appear to be more influential in increasing crime than legitimate support is in reducing it. Moreover, the source of the support (illegitimate), rather than the type of support (expressive, instrumental, illegal skills and connections), and quality of the relationships individuals have with those offering them support, were the more influential factors promoting criminal tendencies. Overall, the current research finds mixed support for the three theories. Merits and limitations to the study as well as suggestions for future research are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".