Governing Climate Change Transnationally: Assessing the Evidence from a Database of Sixty Initiatives
Bibliographic record
Abstract
With this paper we present an analysis of sixty transnational governance initiatives and assess the implications for our understanding of the roles of public and private actors, the legitimacy of governance ‘beyond’ the state, and the North–South dimensions of governing climate change. In the first part of the paper we examine the notion of transnational governance and its applicability in the climate change arena, reflecting on the history and emergence of transnational governance initiatives in this issue area and key areas of debate. In the second part of the paper we present the findings from the database and its analysis. Focusing on three core issues, the roles of public and private actors in governing transnationally, the functions that such initiatives perform, and the ways in which accountability for governing global environmental issues might be achieved, we suggest that significant distinctions are emerging in the universe of transnational climate governance which may have considerable implications for the governing of global environmental issues. In conclusion, we reflect on these findings and the subsequent consequences for the governance of climate change.
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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 | 0.038 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.052 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".