Bibliographic record
Abstract
Typically, the development of gang typologies have used either behaviorally based or structurally based characteristics to develop a classification system of gangs. The current study aims to assess the results of typologies approached from both angles, drawing from the same data source. It also examines whether using a combination of both approaches would prove to be useful. A separate but related aim of this study is to examine the boundaries between self-identified group members and gang members, especially on structural and behavioral characteristics. A hierarchical cluster analysis approach is used to group participants on both behavioral and structural measures using a sample of self-identified gang members ( n = 44) and delinquent group members ( n = 171). A number of important findings emerged from this analysis. First, the “types” of gangs and groups found were not differentiated based on membership status. Second, patterns found strongly depended on the chosen approach (behavioral or structural), but neither proved to be clearly superior. Instead, the choice between the two depend on the interest of the researcher. Finally, using a mixed approach appears to produce the most accurate picture and it does help differentiate between gang and group members more clearly. Yet, a much more complex picture of gangs and groups emerge, which suggests that a purely behavioral or structural classification may sometimes lead to oversimplification and misdirected policy interventions.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| 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".