Exploring the Reciprocal Relationship Between Serious Victimization and Criminogenic Networks
Bibliographic record
Abstract
Reducing explanations of victimization to a person’s risky lifestyle has stalled growth in theories of victimization. Drawing from Carlo Morselli’s contributions to social network analysis, the current study extended past research on community-based co-offending networks and victimization in two ways. First, the current study more comprehensively measured a person’s criminogenic network by also examining the contribution of conflict ties and social ties to victimization. Second, we investigated whether serious victimization was prospectively associated with social network characteristics. Data were used on 99 participants from the Incarcerated Serious Violent Young Offender Study who had criminogenic connections within the city of Surrey, BC. Time-dependent covariate survival analysis was used to model the relationship between network characteristics and time to victimization. Time-series ordinary least squares regression was used to examine whether serious victimization predicted network characteristics. Participants with a greater number of co-offending ties experienced serious victimization significantly later. As evidence of the reciprocal nature of the victimization–network relationship, victimization predicted a greater number of future criminogenic connections in the co-offending tie, social tie, and prison tie networks. Findings have implications for network-based intervention models.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".