Businesses, Green Groups and The Media: The Role of Non-Governmental Organizations in the Climate Change Debate
Bibliographic record
Abstract
The lion's share of media and governmental commentary on the recent Sixth Conference of the Parties (COP-6) to the UN Framework Convention on Climate Change has focused on rifts between the EU and the ‘Umbrella Group’ of countries, including the United States, Canada and Japan, and has led many observers to speculate that intergovernmental negotiations on climate change may have irretrievably broken down. Limiting the focus solely to political difficulties with specific issues, however, emphasizes only part of the story and takes no account of the complex context in which the international negotiations are embedded. This approach does not give sufficient credit to the growing momentum gathering outside the negotiating halls. This article examines recent and rapid changes in attitude and awareness among non-governmental groups-including business and industry, environmental groups and the media-on the issue of global climate change, and the impact these changes have had on the negotiating process and the overall climate change debate. Together these groups provide encouraging signs of a shift in public opinion and ample proof that the failure of the talks in The Hague does not signal the end of the road.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.040 | 0.027 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".