Book Review: <em>Are Perpetrators Under-Researched?</em>
Bibliographic record
Abstract
Are Perpetrators Under-Researched? "Perpetrators remain a comparatively under-researched cohort across the humanities and the social sciences" -so wrote Kjell Anderson and Erin Jessee in the introduction to their 1 2020 anthology, Researching Perpetrators of Genocide.Looking at the rich history of Germanand English-language research on such perpetrators of violence alone, this is a daring thesis.Alternatively, it may be a strategic scientific (marketing) argument, or (more tragically), the authors of these sentences do not know any better.New and English-language perpetrator research, as I like to call it, has been pushing onto the market for some years now under this very label in the form of networks (https://perpetratorstudies.sites.uu.nl/), journals (Journal of Perpetrator research, (https://jpr.winchesteruniversitypress.org/) and corresponding book publications.So, it is time to take a look at their theoretical and empirical offerings.This will be done here on the basis of three anthologies from the last two years. Three Fundamental ProblemsTo begin with, and at the same time to summarize, three fundamental problems must be pointed out that go hand in hand with the focus on perpetrators.Firstly, there is a definition problem: perpetrators can ultimately only be identified based on their actions and the context in which these actions take place.This difficulty and the criticism of it has already led to a massive shift in the formulated research interest.For example, Uğur Ümit Üngör, a leading representative of the new English-language publishing group, has recently made an explicit distinction between perpetration and perpetrators, focusing on the former and thus explicitly on the processuality of the production of collective violence and less on ex post facto Kjell Anderson and Erin Jessee, "Introduction," in Researching Perpetrators of Genocide, eds.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.034 |
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".