The Uneven Geography of Transnational Climate Change Governance
Bibliographic record
Abstract
Introduction Transnational governance by its nature is relatively decentralised and highly dispersed. Literature on the transnational organisations of modes of governance such as networks or PPPs has noted their uneven presence across regions, countries and communities (Andonova & Levy 2003; Andonova 2011; Hale & Held 2011). There is limited understanding, however, about the drivers, patterns and implication of such uneven geographies of climate governance initiatives. In this chapter, we investigate the global geographic patterns that have emerged in the terrain of TCCG. We begin with a brief discussion of the important implications that such patterns may have, and what insights the three analytical lenses used in this book can bring to such an analysis. In the second section of the chapter, we use the three lenses to understand the patterns that appear in the database. We examine the spatial clustering of participation in the TCCG initiatives in our database. As other studies have noted, there is a distinct pattern of North–South relations along certain dimensions of participation, giving rising to a number of normative concerns. However, a rigid North–South understanding of participation in TCCG is also somewhat misleading as we find important regional differences, both in terms of who is engaged in TCCG and where governance activities are carried out. We then consider geographic variation in the kinds of issues that transnational climate change initiatives seek to address and examine the consequences for how TCCG is evolving.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".