Implementation and compliance with human rights law: An exploration of the interplay between the international, regional and national levels 2015-2019
Bibliographic record
Abstract
The Human Rights Law Implementation Project (HRLIP) brought together four academic institutions with a human rights specialism (Bristol, Essex, Middlesex and Pretoria) and the Open Society Justice Initiative (OSJI). The aim of the project was to examine the factors which impact on the implementation of human rights judgments and decisions through a study of nine states across Africa (Burkina Faso, Cameroon and Zambia), the Americas (Canada, Colombia and Guatemala), and Europe (Belgium, the Czech Republic and Georgia). The project traced the implementation of (i) selected decisions deriving from individual complaints to UN treaty bodies; and (ii) selected judgments and decisions of the bodies in the three regional human rights systems. Around ten cases (or clusters of cases) per state were examined in detail, in order to elucidate the factors that tend towards compliance (or non- or partial compliance). In so doing, the HRLIP aimed to provide answers to why states (fail to or only partially) implement rulings, as well as to provide insights which can be used by pro-compliance actors. The HRLIP employed qualitative research methods, combining desk-based document reviews, semi-structured interviews (analysed using NVivo), national and regional workshops, and participant observations at regional and international treaty body meetings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".