Informal Consensus-Building As An Emerging Praxis In International Human Rights Law
Bibliographic record
Abstract
This dissertation examines the practice of informal consensus-building in international law. It defines this practice as a collaborative process through which scholars, advocates, and practitioners, working outside formal institutional mandates, develop shared understandings of legal norms. Drawing on findings from case studies and insider action research in international human rights law, this research demonstrates how such practices can shape the interpretation, implementation, and progressive development of international legal norms. Yet not all such practices are alike, and some have proven more influential than others over time. A comparative analysis of three past initiatives—the 1945 Statement of Essential Human Rights, the 1984 Siracusa Principles on the Limitation and Derogation Provisions in the International Covenant on Civil and Political Rights, and the 1985 Paris Minimum Standards of Human Rights Norms in a State of Emergency—illustrates how the influence of an informal consensus-building initiative is closely tied to the perceived legitimacy of its outputs. That perceived legitimacy, in turn, is shaped both by the strategic choices made during drafting and dissemination and by the broader political and institutional contexts in which these initiatives emerge. To explore this dynamic in contemporary practice, the dissertation examines the development of the 2023 Principles and Guidelines on Human Rights and Public Health Emergencies. As an insider to this consensus-building process, I provide a detailed account of its development, focusing on the tensions between inclusivity, representation, technical rigour, timeliness, and impact. Together, the case studies and action research presented here contribute to scholarly efforts to understand how norms gain legitimacy and traction within communities of practice. Building on the work of Emanuel Adler, Thomas Franck, Magdalena Sepúlveda Carmona, and others, this dissertation underscores both the normative potential and procedural complexity of informal consensus-building as a distinct mode of praxis in international human rights law. It calls for greater scholarly attention to these practices, and for enhanced transparency, inclusivity, and methodological rigour in their development.
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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 | 0.109 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.158 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".