Walking the Line: The Politics of Federalism and Environmental Change
Bibliographic record
Abstract
This short paper looks at the Greenhouse Gas Pollution Pricing Act decision through a wider and more critical jurisprudential lens. In so doing, I demonstrate that the courts are no less political than legislatures in making decisions about who has the constitutional capacity to decide on how the challenges of climate change should be met. This is not so much a criticism of the Supreme Court of Canada, but an inevitable feature of constitutional law. After introducing the traditional and received explanation of the differences between political decision-making and judicial decision-making, I delve deeper into the Court’s opinions and show how they fail to maintain that traditional explanation. I then unravel the political nature and effects of the different visions of federalism that animate the Court’s work in Greenhouse Gas Pollution Pricing Act. Throughout the paper, the connecting trope is that the judges who claim to be walking the constitutional lines are also those who draw those lines: the lines are theirs. It is not that they are not bound by the lines, but that they draw and re-draw the lines as they walk along them. In this way, I will show through the Greenhouse Gas Pollution Pricing Act decision that the Court makes it up (and then remakes it) as it goes along; the lines in the (oil) sands of constitutional law are paths of its own making.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".