The UNFCCC at a Crossroads: Can Increased Involvement of Business and Industry Help Rescue the Multilateral Climate Regime?
Bibliographic record
Abstract
Significant progress in the multilateral negotiations on climate change will only be made if civil society and in particular business and industry stakeholders actively contribute to shape it. Admitted to the international negotiations in the form of non governmental organisations (NGOs), business and industry entities continue however to be far more active at the national than at the international level. Their pro-active investment in new international policy spaces is hence highly warranted. The enhanced participation of the private sector in the multilateral climate regime, however, faces many challenges that will have to be overcome. Lessons on how to achieve an effective involvement may be drawn in particular from the Montreal Protocol on ozone-depleting substances, the World Trade Organisation, the European Union and the Asia-Pacific Partnership on Clean Development and Climate. A preliminary condition for an effective dialogue with business and industry stakeholders is a transparent process. Moreover, systematic consultations with stakeholders should be held, allowing a regular exchange of information and the effective channeling of the expertise of the private sector into the negotiation process.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".