THE EMERGENCE OF MASS ATROCITY CONCEPTS: REFLECTIONS ON GENOCIDE, CRIMES AGAINST HUMANITY, WAR CRIMES AND ETHNIC CLEANSING
Bibliographic record
Abstract
The article commences with a brief listing of some of the key words and phrases used in journalistic accounts in the 1915 New York Times about the mass deportations and killings of ethnic Armenians in the Ottoman Empire. Exploring the emergence of academic and legal terms associated with such mass atrocities in general, a number of key concepts have been formulated, most notably war crimes, crimes against humanity, genocide and ethnic cleansing. Other suggested terms include democide, politicide, ethnocide, urbicide, gendercide and omnicide, which are also briefly discussed by way of background and overview. Amidst an analytical comparison of the meanings of the two terms ethnic cleansing and genocide, problematic aspects of using the term ethnic cleansing are raised and discussed. There has been a continuing global challenge of mass atrocity crimes, and today we witness increased usage of the problematic concept of ethnic cleansing in important, yet diverse case studies such as Nagorno-Karabakh and Gaza. It is suggested that other terms, such as war crimes, crimes against humanity and genocide, are more suitable terms, both analytically and morally.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.066 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".