The Path of Constructing the Supported Decision-Making System in the Interaction Between International and Domestic Law
Bibliographic record
Abstract
This article focuses on the path of constructing the supported decision-making (SDM) system in the interaction between international and domestic law, exploring the role of Article 12 of the Convention on the Rights of Persons with Disabilities (CRPD) in promoting this system globally. The article reviews the theoretical foundation of the supported decision-making system and analyzes the legislative practices in countries such as Germany, Japan, and Canada. It summarizes the interactive model where international law, through soft norms and supervision mechanisms, promotes domestic legal reforms. Article 12 of the CRPD, centered on “equal legal capacity” and “assistance and support,” provides guidance for contracting states in system innovation, advancing legislation through concluding observations and compliance monitoring, and promoting the transformation from the traditional guardianship system to the supported decision-making system. This article argues that the promotion of the supported decision-making system not only reflects international law’s guiding role in domestic law but also demonstrates how domestic practices feedback into and enrich the content of international law. This bidirectional interaction mechanism has laid a solid foundation for achieving the global goal of protecting the rights of persons with disabilities, providing both theoretical justification and practical support for the expansion of the supported decision-making system into other areas of rights protection.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.057 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".