The ‘grow it all’ consensus: structure and policy discourse in Canada’s energy policy-planning network
Bibliographic record
Abstract
Drawing on social network and content analysis, this paper investigates the structure of Canada’s energy policy-planning network and the configuration of advocacy on climate action and decarbonization, highlighting both divisions and shared orientations within the network. We find a structurally integrated network dominated by fossil fuel firms, anchored by interlocks spanning multi-issue and climate-focused think tanks, business councils, and a wide array of energy associations. Closely aligned with network structure are three policy blocs – fossil-opposition, fossil-reform and managed-decarbonization. While the fossil-opposition bloc rejects virtually all forms of climate action, framing mitigation as inherently threatening to economic growth, the fossil-reform and managed-decarbonization blocs coalesce around a ‘grow it all’ energy consensus, characterized by support for continued fossil fuel development alongside the expansion of renewables and other sectors positioned as integral to low-carbon transition, including CCUS, hydrogen, nuclear, and critical minerals. This configuration channels decarbonization pressures into a dominant, cross-sector elite consensus calling simultaneously for the growth of ‘clean energy’ industries and carbon-extractive accumulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.035 | 0.030 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".