Global Human Rights Sanctions: How Can They Contribute to Addressing Mass Atrocities?
Bibliographic record
Abstract
Global Human Rights Sanctions (GHRS) have been used in over 30 countries as a mechanism for imposing unilateral human rights sanctions on individual perpetrators. Despite the hundreds of specific sanctions that have been imposed globally, there remains a lack of understanding about how these measures function on gross human rights violations. This article seeks to explore how GHRS, as an emerging human rights mechanism, contributes to addressing mass atrocities. I categorize the functions of GHRS into two phases: post-atrocity and pre-atrocity. In the post-atrocity phase, I identify three primary objectives asserted by sanctioning states: punishing perpetrators, promoting behavioral change, and providing compensation to victims. In the pre-atrocity phase, I introduce the Swiss Cheese Model to illustrate the deterrent effect of GHRS, emphasizing their unique role in preventing and addressing mass atrocities compared to other human rights mechanisms.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".