The Long Genocide in Upper Mesopotamia: Minority Population Destruction amidst Nation-Building and “International Security”
Bibliographic record
Abstract
“Genocide in Kurdistan” most often refers to events that are recognized as having occurred in Iraq during the late 1980s and early 1990s. Scholars also connect it with events in Turkey’s early years as a republic. This article describes how some scholars’ expansive conception of Kurdistan encompasses regions that also witnessed genocides as large as or larger (in quantitative terms) than these two cases. Armenians, Assyrians, Yezidis, Mandaeans, Shi’i Arabs, and Greek Orthodox Christians witnessed extermination campaigns at various points in Ottoman, Persian, Iraqi, and Turkish history. What is remarkable about Upper Mesopotamia is that these genocides may have reduced native populations in absolute numbers, as compared with ancient or early medieval figures, and to a greater extent than after the genocides of the Kurds or other groups. Like the work of Raphael Lemkin, this article’s analysis will not be limited to the period since the Genocide Convention entered into force. Instead, it presents evidence of continuities between the denationalization strategies, official pretexts, and regions mentioned in histories of the various genocides in Upper Mesopotamia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".