Tracing the Roles of Soft Law in Human Rights
Bibliographic record
Abstract
Abstract This volume explores explore the roles of soft law in both established and emerging human rights regimes, building on a thorough analysis of relevant case studies. This volume claims that a better understanding of how soft law shapes and affects different branches of international human rights law may not only provide a more dynamic picture of the current state of international human rights, it may also help to unsettle and critically question certain political and doctrinal beliefs. The importance of these questions is not just theoretical but also practical. Hence, the book also aims to serve as a guide to human rights practitioners and inform strategic decision-making by surveying the ways in which soft law has been used in concrete cases and by discussing factors that influence the weight accorded to soft law in various contexts. Following two introductory chapters that present the general conceptual framework, the book is divided in two parts. The first part focuses on cases that examine the role of soft law within ‘developed’ human rights regimes (i.e. regimes where there are established hard law standards), its progressive and regressive effects, and the role that different actors play in the incubation process. The second part focuses on the role of soft law in emerging areas of international law, where there is no substantial treaty codification of norms. Chapters in this part examine the relationship between soft and hard law, the role of different actors in formulating new soft law, and the potential for eventual codification.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".