Bibliographic record
Abstract
This article addresses the obstructions that genocide educators face in creating a curriculum that has a preventive impact on learners, before proposing an alternative perspective that would transform the way we approach genocide education, magnifying its capacity to contribute to atrocity prevention. It begins by critiquing the disciplinary emphasis that genocide education currently places on history, which reinforces a notion that genocides are phenomena of the past, rather than the present and future. Next, it addresses the assumption that education automatically and inherently contributes to non-recurrence, arguing that real prevention requires a much more intentional and rigorous approach to education. The article then argues for a turn from genocide education to genocide prevention education, which can and should involve a transdisciplinary approach that takes genocide education out of the silo of history education. For genocide education to contribute optimally to prevention, prevention thinking must be integrated both in the pedagogical approach to teaching about genocide and throughout the disciplinary spectrum in which it is taught. To illustrate this, the article offers two examples of new approaches to achieving this goal that have been developed by two atrocity prevention organizations.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| 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".