Compliance of Territorially Fragile States with International Human Rights Law
Bibliographic record
Abstract
Fragile States are defined as states incapable of fully implementing their international obligations in a part of the territory falling under their jurisdiction. The fragile State's compliance with its international law obligations is then reduced due to objective factors, which also has a major impact on international human rights law (IHRL). However, doctrine has ignored that fragile states can and sometimes do implement their positive obligations in areas beyond their effective control by virtue of the evolving interpretation of IHRL, based on effectiveness. The article argues that each of the dominant conformity theories can only partially explain the factors that influence compliance with IHRL by fragile States: instead of limiting conformity to a monocausal model, rational choices and internal socialization processes should be taken into account to enhance compliance of fragile States. The two main schools of doctrine, rational and constructivist theories, provide complementary explanations to the questions of why and how fragile States can comply with their positive obligations under the IHRL. Rational theories explain that the respect, by fragile States, of their positive obligations in IHRL has direct advantages, especially in terms of monitoring the human rights situation, the well-being of the population of the region and international cooperation. Rational interests do not, however, explain why public bodies act in such a way as to promote the protection of human rights in the area beyond the effective control of the State, especially if their national behavior is not reported in international human rights mechanisms. In these cases, constructivism can provide a complementary explanation: repeated models of norms play an essential role in the creation of a common identity, in particular the belief of national actors in an ideal and active State.
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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 | 0.022 | 0.048 |
| 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 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".