Crime and Family Dynamics: New Perspectives
Bibliographic record
Abstract
Research framework : Studying crime and the family involves considering issues at the individual, relational, community, and social structural levels. Objectives : This issue aims to bring together interdisciplinary perspectives to demonstrate how crime shapes family dynamics, with a particular focus on the intersectionality of social inequalities and forms of structural violence. Methodology : The contributions in this issue include qualitative and quantitative empirical analyses, as well as a social analysis of case law. Results : Problematic and criminalized behavior, and victimization, are linked to gender, social and economic exclusion, parenting practices, peer relationships, and intimate relationships. Crime also involves structural violence: systems, policies, and practices that shape family environments and dynamics. Interventions aimed at suppressing and punishing crime can thus represent a form of structural violence that reproduces the physical, psychological, social, and economic violence experienced within the family. Conclusion : Crime and violence, in all its forms, are subject to structural and social processes that cause these types of “social problems” to cluster together. The family, as a basic unit of the social system, can promote resilience or reproduce violence. Interventions that ignore families’ “thread of trauma” risk reproducing violence, thereby shaping family trajectories. Contribution : This issue demonstrates that crime is embedded in relational, social, and institutional dynamics. The application of an interdisciplinary and intersectional approach allows for the analysis of the complexity of family experiences of crime.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".