Decarbonization, hegemonic projects, and the green growth policy-planning network: the case of Québec
Bibliographic record
Abstract
Since 2010, grassroots-led socio-ecological movements in Québec, Canada played a key role in overturning carbon extractivist proposals. Building on their successes, these groups now aim to move energy transition debates toward a broader conception of transition that includes radical social justice and post-capitalist alternatives. Meanwhile, corporate actors and the state enlisted major environmental NGOs and union federations into various technocentric ‘green growth’ projects. These hegemonic and counter-hegemonic struggles define how transition unfolds in the province, yet few have studied how actual social actors organize to carry out these divergent responses to the climate crisis. We develop a structural analysis of the green growth policy-planning network in Québec. Starting from five organizations at the core of transition debates, we analyze the network of board interlocks they are embedded in. We describe the overall structure of the network and its main corporate, civil society, and individual actors. Analysis outlines the possibility of a new hegemonic bloc forming, positioned around the green growth project and the cleantech sector, close to achieving dominance in Quebec, that would threaten deeper decarbonization efforts. Thus, despite the recent ban on petroleum extraction, like elsewhere, energy transition in Quebec still faces deep social and ecological contradictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".