Constructing Transnational Climate Change Governance Issues and Producing Governance Spaces
Bibliographic record
Abstract
Introduction TCCG is concerned with a range of issues (see Chapter 2), from the familiar agendas of promoting renewable energy technologies and the creation of carbon markets to issues of investment risk, food security and water services with which mainstream climate change governance has had limited involvement. Despite this diversity, we find four predominant sets of issues with which the majority of initiatives are concerned: (1) energy; (2) carbon markets and finance; (3) carbon sequestration and forests; and (4) infrastructure (transport, waste and water projects and systems) (Chapter 2, Figure 2.4). This chapter explores how and why TCCG focuses on these issues and their potential consequences for understanding climate governance more broadly. First, we examine the nature of the four sets of issues that have come to provide the focus for TCCG activity, and we ask why they have become the focus of activity. While such TCCG patterns are in common with (or connected to) other areas of climate governance, they also have features distinct to both the character of TCCG itself and the nature of the particular issue areas. The second section of the chapter explores how transnational activity around these issues is organised – which types of TCCG arrangement are engaged in which issue areas – and how such areas combine in interesting and sometimes counterintuitive ways. Using a cluster analysis technique to group initiatives, the resulting combinations show no intuitive or natural patterns of TCCG. Rather, TCCG initiatives are put together in much messier ways by particular sorts of actors pursuing particular agendas, combining pre-existing policy and institutional fields with climate change to produce distinct clusters of activity in the TCCG arena.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".