The Outcome of the Negotiations on the Global Pact for the Environment: A Commentary
Bibliographic record
Abstract
In May 2018, the United Nations General Assembly adopted the resolution “Towards a Global Pact for the Environment”. This resolution established an intergovernmental working group to discuss the opportunity to open treaty negotiations to codify the fundamental principles of international environmental law into a treaty dubbed the Global Pact for the Environment. In May 2019, the intergovernmental working group completed its mandate and adopted a set of recommendations that were formally endorsed by the United Nations General Assembly in August 2019. Contrarily to what the supporters of the Global Pact for the Environment project had hoped for, the working group only recommended the preparation of a “political declaration” without referring to the codification of the principles of international environmental law. This paper offers a critical commentary of the outcome of these negotiations. The analysis suggests that the decision to elaborate a Global Pact for the Environment would have entailed considerable risks for international environmental law and that if adopted, this instrument would not have necessarily helped to increase the problem-solving capacity of international environmental law. Based on the language used in the recommendation to prepare a “political declaration”, the paper also discusses some of the key elements that could shape and inform the upcoming negotiations of this declaration.
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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 | 0.035 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.037 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.078 | 0.085 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".