Corporate Climate Policy-planning in the Global Polity: A Network Analysis
Bibliographic record
Abstract
Alongside the climate change denial movement, a section of the capitalist class has been organizing to promote a project of “climate capitalism” that relies on carbon markets and other policies compatible with the neoliberal order to reduce greenhouse gas emissions. Like the denial movement, promoters of climate capitalism have constructed an extensive network of think tanks and policy-planning groups to foster adherence to their climate policy proposals. This article uses social network analysis to map out the reach of these climate and environmental policy groups within the array of interconnected NGOs, inter-governmental organizations, philanthropic foundations, and other organizations that constitute the global polity. This analysis sheds light on the position climate capitalism—understood as a project of a section of the global corporate elite—occupies among international organizations. Overall, I find that climate and environmental policy groups: (1) maintain substantial ties to key organizations of the global polity, and (2) mediate a substantial amount of relations, bridging between central organizations and more peripheral ones, as well as among those located in Europe and North America. I thus argue that a global inter-organizational infrastructure exists that supports climate capitalism, which contributes to its dominant position in climate change politics.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".