Introduction: the United Nations and targeted sanctions
Bibliographic record
Abstract
Mandatory United Nation (UN) sanctions imposed against Iraq, the Federal Republic of Yugoslavia and Haiti in the 1990s gave rise to strong criticisms because of their comprehensive nature and the harm inflicted on innocent civilians. Chastened, the international community, led by like-minded, mainly western states, reformed the instrument of sanctions and adopted ‘targeted sanctions’—measures designed to address the peace spoilers but limit damage to the population at large.1 The consequence has been the evolution of sanctions from blunt, comprehensive measures targeting the economies of entire states to more specific measures targeting individuals, non-state entities, particular regions and specific sectors of economies. Despite this profound change, sanctions are still mistakenly assumed to target whole countries. Failing to recognize these qualitative differences between comprehensive and targeted sanctions has prevented the debate on sanctions from evolving. Acknowledging the centrality and the novel characteristics of contemporary sanctions, the Targeted Sanctions Consortium (TSC) has collected evidence from all cases of UN targeted sanctions since the end of the Cold War. The intent is to provide an empirical basis for bridging the gap between practice and theory on the centrality of sanctions as instruments of governance in the hands of the UN Security Council. Led by Thomas Biersteker and Sue Eckert and involving over 60 international scholars and practitioners, the database includes all 23 cases of targeted sanctions imposed by the United Nations since 1991. The cases have been sub-divided into episodes representing the different objectives and sanctions measures that were imposed over the duration of a sanctions case. Thus the database is composed of 63 episodes of targeted sanctions. Each episode was coded using 296 variables in order to allow cross-case analyses and thorough within-case comparisons. The variables represent 15 different categories that describe each episode in great detail. Data feature, among other factors, the presence of other foreign policy instruments, concurrent sanctions by other actors, the drafters of the UN Security Council sanctions resolutions, the range of objectives pursued by the sanctions, evasion techniques, unintended consequences observed and, ultimately, an evaluation of the sanctions' effectiveness.2
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".