Underrepresented developing States, marginalized communities, big business and procedural injustice – how equal were the UN INC-5.2 Global Plastic Treaty negotiations?
Bibliographic record
Abstract
Abstract Negotiations by more than 180 States during the Intergovernmental Negotiating Committee (INC) session 5.2 to develop a legally binding instrument with the purpose to end plastic pollution have, once again, concluded without a treaty. This is especially disastrous for developing States and marginalized peoples (such as indigenous communities and waste pickers), who are disproportionately suffering from plastic pollution. In this article, we show that developing States were underrepresented at the INC-5.2 negotiations in Geneva: Their delegations were on average only half as large (~5 delegates) when compared to delegations from Western European States (~13 delegates) and those from States with a high and very high Human Development Index (~10 delegates). In addition, more than 230 industry representatives participated in INC-5.2, exerting influence in diverse ways, both during official negotiations and through side events, organized by lobbying organizations. Finally, we discuss the importance of how treaty negotiations were organized: Simultaneously occurring negotiation formats (such as contact groups and informal meetings) put smaller delegations at a disadvantage, causing procedural injustice, which falls under the responsibility of the INC Secretariat.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".