Striking at the Root Problem of Canadian Environmental Law: Identifying and Escaping Regulatory Capture
Bibliographic record
Abstract
Our government is corrupt. We all know this. But we tend to ignore this fact or treat it as an analytical afterthought; regrettable, perhaps, but seemingly insoluble. If we are to reform Canadian environmental law, however, we can no longer afford to ignore this fundamental problem. This paper argues that regulatory capture – the direction of regulation in law or application away from the public interest toward the private interests of regulated industries – is the systemic root problem underlying Canadian environmental law and policy. This systemic root problem is all the more complex because of its catch-22 nature, whereby the political barriers necessitating law reform in the first place render proposed reforms and policy recommendations politically impossible to implement. Canadian environmental law scholarship must seek a way directly through this problem. By tracing the contours of the oil and gas industry’s capture of Canada’s greenhouse gas emissions and climate change policy, this paper suggests a new way forward for scholarship and public policy engagement seeking to redirect legislation and regulation away from the private interests of industry to the public interest of citizens and thereby close the democratic deficit in Canadian environmental law and policy.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.036 | 0.051 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".