Bibliographic record
Abstract
Scholars are critical of how economists overlook “the questions of genocide,” and of how legislatures have not paid adequate attention to the subject of looting, except in the case of the Armenian genocide. This article, informed by interdisciplinary perspectives, uses government documents, data, and semi-structured interviews to discuss the overlooked triangle of looting, economics, and the Anfal genocide of the Kurds in Iraq. The study refuses to limit itself only to the eight stages of the Anfal genocide that started in 1988, and instead offers data on its preliminary phases which occurred earlier in the 1980s. It then discusses the multidimensional political economy of the Anfal genocide and argues that (a) the legalized plundering of spoils of warby the Ba’ath regime served as a political economic strategy to justify the Anfal genocide; (b) Saddam Hussein utilized economic prospect theory—putting a higher emphasis on imagined gains than on losses—by maximizing revenue and minimizing the cost of the genocide; and (c) Saddam’s use of symbolic religious names and Qur’an verses did not demonstrate his religious commitment, but rather aimed to foster and restore the cultural legacy of looting among ordinary people. The article focuses on rewards, in the form of economic capital earned from looting and confiscations, as goals that aided the effective execution of the Anfal genocide and promoted divisions within urban Kurdish society but that failed to deracinate Kurdish resistance culture.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".