Between Scylla and Charybdis: the implications of the human right to science for regulating the harms and benefits of environmental science and technology
Bibliographic record
Abstract
This article explores whether the integration of human rights approaches, in particular, the human right to science in Article 15(1)(b) of the International Covenant of Economic, Social and Cultural Rights, offers a basis for improving upon current approaches in international environmental law by widening democratic input and oversight in decisions involving environmental science and its applications. It examines a case study regarding the international regulation of marine geoengineering under an amendment to the 1996 Protocol to the 1972 Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matter. The analysis focuses on how the harms and benefits of marine geoengineering research are conceived of in the amendment, and the norms and processes adopted to address them. These same issues are then examined under the human right to science, focusing on the recent interpretation of the right by the Committee on Economic, Social and Cultural Rights in its General Comment No. 25. It seeks to show in a particular case how international environmental law and international human rights law each bring to bear different objectives, norms, and processes in how they treat issues of environmental science and technology, and explores the benefits of more integrated approach.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.068 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.024 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 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".