Social networks and gangs: moving research forward with low-cost data collection opportunities in school and prison settings
Bibliographic record
Abstract
Purpose As useful as police data have been in furthering our knowledge of gangs and gang violence networks, not everything about gang networks can be learned from examining police data alone. There are numerous alternative sources of data that already exist on gang networks and some that can be developed further. This study aims to introduce existing research on social networks and gangs with a specific focus on prisons and schools. Design/methodology/approach This study reviews the existing empirical literature on gang networks in schools and prison settings and use the broader literature on social networks and crime to propose directions for future research, including specific suggestions on data collection opportunities that are considered to be low-cost; that is, strategies that simply make use of existing administrative records in both settings, instead of developing original data collection procedures. Findings The author found the existing literature on each of these settings to be quite limited, especially when the spotlight is put specifically on gang networks. These shortcomings can be addressed via low-cost opportunities for data collection in each of these settings, opportunities that simply require the network coding of existing administrative records as a foundation for gang network studies. Originality/value Investing in these low-cost network data collection activities have the potential for theoretical and empirical contributions on our understanding of gang networks, and may also bring value to practitioners working in school and prison settings as a guide for network-based planning or interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".