Governing Climate Change Policy: From Scientific Obscurity to Foreign Policy Prominence
Bibliographic record
Abstract
F or many years, climate change was a technical issue that only concerned a small number of scientists and policymakers. 1 At the 1988 Toronto Conference on the Changing Atmosphere (arguably the first important policy-oriented global climate change forum), the prime ministers of Canada and Norway proposed a global “law of the air” and called for the 20 percent reduction in global emissions of carbon dioxide by the year 2005. Even at that late date, the United States dismissed the importance of this conference by sending only a mid-level government representative. While the United States did not think that the Toronto meeting warranted a higher diplomatic presence, the delegations of some countries were headed by their respective prime ministers or presidents. When the concluding resolution of the Toronto meeting called for a fundamental reassessment of global priorities and responsibilities, William Nitze, then the Deputy Assistant Secretary of State for Environment, Health, and Natural Resources, argued that it “would be premature at the current moment to contemplate an international agreement that sets targets for greenhouse gases.” 2 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".