Non-Governmental Organizations' Efforts to Protect Stratospheric Ozone
Bibliographic record
Abstract
In 1987, the international community adopted the Montreal Protocol on Substances that Deplete the Ozone Layer – subsequently labelled as one of the most successful environmental treaties of our time. In 1992, Peter Haas emerged with a study on epistemic communities and their efforts to protect stratospheric ozone. According to Haas’ research, epistemic community members – consisting of United States government officials and atmospheric scientists from the international community – affected the U.S. national stance for rulings on ozone depleting CFC substances, eventually leading to the adoption of stringent regulations through the Montreal Protocol. However, in this essay it is argued that Haas’ work offers only a limited explanation of the processes that led to the formation of the Montreal Protocol and its subsequent ratifications due to the fact that activities of non-governmental organizations have not been sufficiently well acknowledged. It is argued that NGOs played a vital role in the process by: (1) partaking in governmental lobbying, (2) raising public awareness of ozone depletion, (3) endorsing the usage of environmentally friendly alternatives to CFC substances, and (4) affecting the outcome of the Second Meeting of the Parties in London 1990, when the weak stipulations of the first rendition of the Montreal Protocol were considerably strengthened.
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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".