Bibliographic record
Abstract
Abstract The article discusses the impact of state false compliance on the translation of international norms. Using the implementation of the free, prior, and informed consent ( fpic ) norm in Paraguay as an example, it aims to demonstrate how false compliance hinders the meaningful translation of norms into practical mechanisms, policies, and laws, ultimately silencing indigenous voices. The paper focuses on Paraguay as a case study of a “false-ratifier,” a state that appears to comply with international treaties but does not genuinely adhere to their objectives. While Paraguay ratified relevant conventions and adopted protocols related to fpic , its actual commitment to this norm remains limited. The state’s interpretation and implementation of fpic are influenced by a production-oriented perspective, leading to consultations that lack genuine consent. In Paraguay’s case, the state’s fpic implementation remains superficial, negatively impacting the ability of indigenous peoples to participate effectively. The main argument presented is that compliance extends beyond ratification; it involves the translation of international obligations into national laws and policies. False compliance can result in insufficient translation, enabling states to evade their responsibilities while maintaining the appearance of compliance. This approach can impede non-state actors from holding the state accountable, as demonstrated in the Paraguayan case.
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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 | 0.189 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".