Bibliographic record
Abstract
Abstract This article sets the stage for a special issue exploring group-level dynamics and their role in producing violence. My analytic focus is socialization, or the process through which actors adopt the norms and rules of a given community. I argue that it is key to understanding violence in many settings, including civil war, national militaries, post-conflict societies and urban gangs. While socialization theory has a long history in the social sciences, I do not simply pull it off the shelf, but instead rethink core features of it. Operating in a theory-building mode and drawing upon insights from other disciplines, I highlight its layered and multiple nature, the role of instrumental calculation in it and several relevant mechanisms – from persuasion, to organized rituals, to sexual violence, to violent display. Equally important, I theorize instances where socialization is resisted, as well as the (varying) staying power of norms and practices in an individual who leaves the group. Empirically, the special issue explores the link between socialization and violence in paramilitary patrols in Guatemala; vigilantes in the Bosnian civil war; gangs in post-conflict Nicaragua; rebel groups in the Democratic Republic of Congo, El Salvador, Sierra Leone and Uganda; post-conflict peacekeepers; and the US and Israeli military. By documenting this link, we contribute to an emerging research program on group dynamics and conflict.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".