Author comment: Indigenous rights, knowledge, and participation in the global plastics treaty — R1/PR5
Bibliographic record
Abstract
Indigenous Peoples are disproportionately impacted at every stage of the plastic lifecycle, from the extraction of the fossil fuel feedstock and plastic production, to the widespread dispersal of maco-, micro- and nanoplastics in the natural environment. They face many barriers to their participation in UN processes and must constantly push for their rights to be upheld and for their full and effective participation to be secured. This constant basic struggle for Indigenous rights and participation can consume all the energy and efforts of Indigenous delegates in UN processes at the expense of all the other important knowledge and messages they carry from their communities and nations to address the very real and serious harms that have been inflicted on their territories and all the life within it. Negotiators at INC-5.2 have a great responsibility to address this serious global crisis, while being reminded that Indigenous Peoples, who are on the frontlines of the plastic pollution crisis, must be equal participants as experts of their own knowledge and science and participate in the process as rightsholders in all decision-making that affects them.
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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.057 | 0.033 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".