Update on the work of the Special Rapporteur on Human Rights and the Environment: relevance for states, businesses, and local environmental justice
Bibliographic record
Abstract
In this, my second post on the Dalhousie Environmental Law News blog, I am joined by JD candidate Meg Williams. In my first post, I provided reflections on the way in which environment and climate justice issues were – or were not – incorporated into discussions at the UN Forum on Business and Human Rights, held in Geneva in November 2018. At the time I noted that Mr. Baskut Tuncak, the Special Rapporteur on human rights and hazardous substances, had spoken at length about a 2015 report on the right to information at the Geneva forum. Mr. Tuncak drew attention to the independent responsibility of businesses to undertake human rights due diligence to identify actual and potential impacts of hazardous substances on human rights to life and health. Businesses would then be expected to communicate to governments and the public about the existence of these substances in products and global supply chains. In this post, we will first reflect on the recent work of a different United Nations Human Rights Council Special Rapporteur, the Special Rapporteur on human rights and the environment (officially, the special rapporteur on the issue of human rights obligations relating to the enjoyment of a safe, clean, healthy and sustainable environment). We will then consider the implications of this and select contributions by other recent Human Rights Council mandate holders for local environmental justice concerns.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".