Bibliographic record
Abstract
All major theories of ethnic violence provide insight that is simultaneously limited and valuable. Each theory is limited because ethnic violence is so complex: it depends on the particular context and sequence of events; any number of factors can spark it; and the perpetrators of ethnic violence are all motivated by different combinations of grievances, aspirations, interests, obligations, and pressures. Because of this very complexity, however, multiple theoretical perspectives are able to simultaneously offer insight into ethnic violence, making them all valuable. Noting the value and limitations of theories of ethnic violence, the goal of this chapter is not to construct a general theory of ethnic violence. Instead, I construct a more focused framework that considers how education affects ethnic violence. For this, I note mechanisms from a variety of theories, consider how the mechanisms affect one another, and explore scope conditions on which the mechanisms depend. This mid-range theoretical approach follows recent claims by diverse social scientists that theory is most useful when it explores mechanisms and scope conditions instead of covering laws (George and Bennett 2005; Hedstrom and Swedberg 1998; Rueschemeyer 2009; Tilly 2001). Before turning to the theoretical framework, however, it is instructive to first consider the two complex concepts that are its focus: ethnic violence and education.
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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 | 0.000 | 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 | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".