The Human Right to Science and Its Relationship to International Environmental Law
Bibliographic record
Abstract
Abstract This article explores the potential contribution of international human rights law – specifically, the oft-neglected ‘right to science’ – to the interpretation, operation and progressive development of international environmental law. Science and its applications play a critical role in environmental protection. At the same time, society faces persistent controversies at this interface. Environmental regimes may lack sufficient norms and tools for regulating upstream science and innovation processes because they tend to focus narrowly on physical harms to the environment and may not address the wider ethical, legal, social and political concerns. The human right to science, which is codified in various international and regional human rights instruments, may serve to augment international environmental law and contribute to more effective, equitable and democratically legitimate and accountable processes and outcomes in relation to the application of science and technology in environmental regimes. The article begins by outlining the scope and contents of, as well as the limitations on, the right to science, focusing on Article 15(1)(b) of the International Covenant on Economic, Social and Cultural Rights (ICESCR) and its overlaps with the norms of international environmental law.1 It then analyses the ways in which the right to science may influence the development of international environmental law by elucidating mechanisms for the integration of a human rights perspective in science and technology and by outlining its potential substantive contributions to the development of international environmental law.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.081 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".