It’s not as simple as copy/paste: the EU’s reproduction of the High Ambition Coalition in international climate governance
Bibliographic record
Abstract
Following the success of the High Ambition Coalition (HAC) in contributing to the Paris Agreement at COP 21 of United Nations Framework Convention on Climate Change (UNFCCC) in 2015, the European Union (EU), along with other partners, sought to remobilise the HAC in pursuit of two key international climate agreements in 2016: the Kigali Amendment to the Montreal Protocol and the ICAO Carbon Offsetting and Reduction Scheme for International Aviation (2016). However, despite these negotiations taking place simultaneously within a political push for climate action following the Paris Agreement, the EU’s use of the HAC produced mixed results. While the HAC contributed to reaching a final agreement in Kigali, this did not appear to be the case for ICAO CORSIA. Considering the EU’s continued leadership ambitions in global climate governance and its subsequent focus on coalitions, it is essential to understand the precise contextual conditions that affected its use of the HAC. This paper therefore answers the question Why was the EU successful in its use of the High Ambition Coalition in the negotiations leading to the Kigali Amendment yet unsuccessful in the ICAO CORSIA negotiations? It relies on 22 semi-structured interviews with EU, EU member state, and third state officials involved with the coalition efforts in the negotiations, as well as official EU and coalition documents and press reports. In comparing the two cases, it identifies three contextual conditions that needed to be present for the HAC to be successful: sufficient time to influence the negotiations, a relatively strong implication of the EU’s HAC partners in the negotiating forum in question, and a general awareness and prioritization of climate change therein.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".