Climate Ambition and Sustainable Development for a New Decade: A Catalytic Framework
Bibliographic record
Abstract
Abstract This paper examines the Global Climate Action Agenda (GCAA) and discusses options to improve sub‐ and non‐state involvement in post‐2020 climate governance. A framework that stimulates sub‐ and non‐state action is a necessary complement to national governmental action, as the latter falls short of achieving low‐carbon and climate‐resilient development as envisaged in the Paris Agreement. Applying design principles for an ideal‐type orchestration framework, we review literature and gather expert judgements to assess whether the GCAA has been collaborative, comprehensive, evaluative and catalytic. Results show that there has been greater coordination among orchestrators, for instance in the organization of events. However, mobilization efforts remain event‐driven and too little effort is invested in understanding the progress of sub‐ and non‐state action. Data collection has improved, although more sophisticated indicators are needed to evaluate climate and sustainable development impacts. Finally, the GCAA has recorded more action, but relatively little by actors in developing countries. As the world seeks to recover from the COVID‐19 crisis and enters a new decade of climate action, the GCAA could make a vital contribution in challenging times by helping governments keep and enhance climate commitments; strengthening capacity for sub‐ and non‐state action; enabling accountability; and realizing sustainable development.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".