Paths Not Traveled: Genocide Prevention, the Global Grassroots, and the Power of Dialogism
Bibliographic record
Abstract
Genocide prevention has been a field located in the Global North but preoccupied with the Global South. It is an elite field that is dominated by Western technical experts, many of whom have close ties to Western governments, and that is organized along vertical hierarchies. It works largely with states and militaries, and focuses a majority of its attention on military intervention into ongoing conflict as well as legal accountability after genocide has been committed. These priorities have had a dramatic impact on how genocide is defined and identified, preferencing the mass killing element of the crime and “reducing genocide to law,” as the legal scholar Payam Akhavan aptly put it in his 2012 book of that title. Although grassroots outreach is sometimes advocated, it is usually understood in terms of pressure politics and lobbying at the center of global power rather than as the empowerment of ordinary people worldwide as transformative and preventative agents in and of themselves. This article is both a call for critical self-examination of the field of genocide studies and a surfacing of paths not taken in the practice of genocide prevention.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.085 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".