Wealth Drain and Value Transfer: A Study of the Mechanisms, Harms, and Beneficiaries of the Sanctions Regime on Iran
Bibliographic record
Abstract
This dissertation offers a critical examination of the practices and techniques of economic sanctions as 'slow and structural violence' in and through international law.These coercive measures have been constructed and deployed by the global North to impose de-development and facilitate wealth drain from the periphery to the imperial core.At its heart, this study challenges the conventional and reductionist understanding of economic sanctions as 'peaceful alternatives to war', and their systematically deliberate violence as 'unintended consequences'.Grounded in the context of the sanctions regime against Iran, my research traces its origins to 1950s-when Iran first asserted sovereignty over its natural resources-and examines the intensification of these sanctions after the 1979 Iranian revolution.I develop a critical account of sanctions, examining how these coercive measures operate as legal, political, and economic instruments of imperialism.This involves a comprehensive critical analysis of the violence inherent within the structures of these sanctions, offering a detailed examination of their mechanisms, justifications, harms, and beneficiaries. ______________________________________________________________________________Alhamdulillahi Rabbil-'alamin.I begin by thanking my amazing co-supervisors, Christiane Wilke and Umut Özsu.Christiane, thank you for helping me grow, not only as a scholar but also as a person.Umut, your generosity and insights have been a guiding light, instrumental in helping me find my intellectual path.To both of you, I extend my deepest gratitude.You represent all that is good in academia
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".