UNEP and Marine & Environmental Law Institute, "Plastics Toolbox: Business, Human Rights, and the Environment" (last updated November 2021) (Dalhousie University, Schulich School of Law)
Bibliographic record
Abstract
The Plastics Toolbox: Business, Human Rights, and the Environment, compiles good practices and cross-cutting guidance on a human rights-based approach to plastic pollution prevention and management with a focus on capacity building of governments and businesses in the East Asian Seas region. This compilation of resources, guidance, tools and trainings was prepared by a team of researchers at Dalhousie University's Marine and Environmental Law Institute under the direction of project lead Dr Sara L Seck, with funding from the United Nations Environment Programme. The materials in the toolbox were gathered from May to August 2021 and updated in November 2021. The toolbox will be updated in 2022.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.013 |
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".