The Crude Politics of Carbon Pricing, Pipelines, and Environmental Assessment
Bibliographic record
Abstract
In this article, I attempt to unpack a particularly problematic “peril of pipelines and riddle of resources,” the theme of this special issue: namely, the simultaneous acknowledgement of the need to act urgently and ambitiously on climate change, on the one hand, and on the other hand the decision – taken over and over again – to delay meaningful action by disputing narrow but largely settled questions of jurisdiction and responsibility while steadfastly supporting and subsidizing expanded fossil fuels production and export. These disputes delay and distract us from the kinds of complex and controversial policy choices that we need to debate and decide. Delay courts – quite literally – disaster. My argument in this article is that the constitutional law and law reform arguments made in respect of carbon pricing, pipeline approvals and regulations, and environmental assessment processes are inescapably political. On the one hand, legal arguments about the “pith and substance” of each are necessarily normative and ineluctably bound up in competing ideologies, values, and public policy perspectives on Canada’s social and economic priorities. On the other hand, those same legal “pith and substance” arguments are being “weaponized,” not out of genuine, good-faith disagreements over legal doctrine, but as indirect, collateral attacks on the very prospect of urgent and ambitious climate change policymaking.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".