Bibliographic record
Abstract
Abstract How do people come to participate in violent display? By ‘violent display’, I mean a collective effort to stage violence for people to see, notice, or take in. Violent displays occur in diverse contexts and involve a range of actors: state and non-state, men and women, adults and children. The puzzle is why they occur at all given the risks and costs. Socialization helps to resolve this puzzle by showing how actors who have consciously adopted or internalized group norms might take part, despite the risks. Socialization is more limited in explaining how and why actors who are not bound by group norms also manage to put violence on display. To account for these other pathways, I propose a theory of ‘casting’. Casting is the process by which actors take on roles and roles take on actors. Roles enable actors to do things they would not normally do. They give the display its form, content, and meaning. Paying attention to this process reveals how violent displays come into being and how the most eager actors as well as unwitting and unwilling participants come to take part in these grisly shows. To explore variation in the casting process, I investigate violent displays that occurred in two different contexts: the Bosnian war and Jim Crow Maryland. Data come from interviews, trial testimonies, and primary sources.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".