Cases Studied in <em>Genocide Studies and Prevention</em> and <em>Journal of Genocide Research</em> and Implications for the Field of Genocide Studies
Bibliographic record
Abstract
The adoption of the Genocide Convention in 1948 was accompanied by the emergence of genocide as a field of study, first in the form of Holocaust Studies, followed by Genocide Studies, then Comparative Genocide Studies and, most recently, Critical Genocide Studies. Over the last 20-30 years, the field of genocide studies has greatly expanded. According to Alexander Hinton, “As the outlines of the field emerge more clearly, the time is right to engage in critical reflections about the state of the field.” This article seeks to enhance the field of genocide studies by answering Hinton’s call for reflective analysis. It does so by analyzing every original research article published in the Journal of Genocide Research (1999-2018) and Genocide Studies and Prevention (2006-2018), based on case of genocide studied; the canon location of the case; method of genocide; and the type of government of the perpetrator. The results of this research show that the field remains dominated by particular understandings genocide and which types of governments are most associated with the crime.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".