Bibliographic record
Abstract
This article explores the political involvement of transnational corporations and their directors in elaborating the project of ‘climate capitalism’ advanced to address climate change. Climate capitalism seeks to redirect investments from fossil energy to renewable energy generation so as to foster an ecological modernization of production and reduce greenhouse gas (GHG) emissions. I use social network analysis to assess the potential for climate capitalism, as a project of a section of the corporate elite, to replace the current ‘carboniferous capitalist’ regime. Corporate-funded climate and environmental policy groups (CEPGs) constitute major venues for the corporate elite to assemble and plan their response to the climate crisis. By mapping out the network of board-level interlocks between CEPGs and the largest transnational corporations, I first find that certain CEPGs are centrally located among the global intercorporate network, and thus well positioned to promote climate capitalism among the corporate elite. Second, I delineate a climate capitalist inner circle that includes the individual members of the corporate community who arguably are able to exert the greatest power to shape climate capitalism. However, many of them, close to the oil and nuclear sectors, may support a long-term transition away from fossil fuels, incompatible with avoiding dangerous climatic warming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".