Strategies for Compliance with Non-Binding International Decisions: The Spanish Case
Bibliographic record
Abstract
The Spanish State is among the countries with high standards of compliance with its international obligations on human rights. Regardless of the reasons that explain the respect for such commitments, it is surprising to note that the degree of compliance differs depending on the international guarantee mechanism. Compliance with treaties whose oversights is attributed to a court, such as the European Court of Human Rights, is stricter than compliance with covenants under the United Nations, whose supervision is recognised to the committee established by each treaty or by their annexed protocols. Even though the Committees also respond to individual complaints raised by persons subject to the Spanish State jurisdiction, the binding nature of their resolutions is questioned by some State institutions. Both members of the executive and the judiciary. This paper analyses the grounds that justify this two-speed compliance and questions the institutional arguments that support such reality. Our reflection insists that the obligations derived from all human rights treaties ratified by Spain are identical and demand an equal response from the State institutions. The international human rights law and the Spanish Constitution itself so require it.
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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 | 0.039 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".