Green dreams or fossil schemes? Mapping Canada's green growth policy-planning network
Bibliographic record
Abstract
Green growth is a leading framework and project for addressing climate change. While social science research has challenged the possibility of green growth and the proposed means of achieving it, less work has examined the actors and business sectors that define, support and mobilize the project in different regions. Employing a neoGramscian lens and using tools of social network analysis and content analysis, this paper maps a green growth policy-planning network in Canada and considers its potential to support energy transition. It finds that while a tightly knit coalition has formed in support of green growth, fossil fuel firms and banks that heavily fund fossil fuel projects are central in the network. Consistent with ties to carbon firms, central policy organizations advance solutions that sustain the viability of the fossil fuel sector, as they emphasize the merits of reducing production emissions from fossil fuels, while avoiding or opposing policy to phase out the industry. The findings indicate a network that accommodates pressures for decarbonization while delaying energy transition. The Canadian case highlights the failure of green growth to challenge fossil fuel incumbents and vested interests, pointing to a key limitation of the project, especially in fossil fuel producing regions. • A cohesive green growth policy-planning network has emerged in Canada. • Fossil fuel corporations and their major financiers occupy central positions within this network. • Prominent organizations in the network advance solutions that preserve fossil fuel production. • The network responds to demands for decarbonization while delaying energy transition. • Canada's experience underscores green growth's inability to disrupt entrenched fossil fuel interests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".