Bibliographic record
Abstract
Over the last two decades, the interdisciplinary field of genocide studies has dramatically expanded and matured. No longer in the shadow of Holocaust studies, it is now the primary subject of journals, textbooks, encyclopedias, readers, handbooks, special journal issues, bibliographies, workshops, seminars, conference, Web sites, research centers, government agencies, non-governmental organizations, international organizations, and a unit at the United Nations. If not yet fully theorized, the discipline is characterized by a number of debates and approaches. As the outlines of the field emerge more clearly, the time is right to engage in critical reflections about the state of the field, or what might be called critical genocide studies. The goal is not to be critical in a negative sense but to consider, even as a canon becomes ensconced, what is said and unsaid, who has voice and who is silenced, and how such questions may be linked to issues of power and knowledge. It is, in other words, a call for critical thinking about the field of genocide studies itself, exploring our presuppositions, decentering our biases, and throwing light on blind spots in the hope of further enriching this dynamic field.
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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.059 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".