Case Studies in Canadian Regulatory Review: Recommendations for a Better Policy & Process
Bibliographic record
Abstract
Infrastructure projects in Canada have become highly divisive across regional, political and cultural lines. Regulatory reviews have been slowed or halted due to public opposition, consultative mismanagement and a poor or incomplete understanding of where legal authority lies and how it must be used. Current policy approaches to consultation are unequal to the task of allowing stakeholders to work through these points of contention in an atmosphere of efficient consensus building. Using three specific examples of stalled energy infrastructure projects, this capstone presents process reform options for improving the efficiency and effectiveness of the consultation process. Its opening section begins by outlining the political perspectives shaping energy infrastructure approval debates, showing how economic and environmental concerns are central points of contention among stakeholders. It then examines the intersection of culture, law, and economics, showing how the values and norms of Aboriginal communities are ingrained in legal frameworks that confer a duty to consult on the government. The same economic and environmental arguments used in the energy infrastructure debate also shape these legally mandated, culturally informed discussions. The opening section then closes with an examination of the laws, policies, and organizations that presently structure the processes of consultation. The central argument through this account of energy infrastructure contention is that present policy approaches are inadequate and require reform. The second section of the capstone presents three case studies of stalled or failed energy infrastructure approvals, highlighting in each case the specific challenges and shortcomings of existing approaches and processes. A common thread throughout all three is the inadequacy of current policy to facilitate meaningful conversations. The closing section of the capstone draws on a range of scholarly literatures to present options for meeting the challenges described in the first two sections.
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 | 0.214 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.048 | 0.019 |
| Scholarly communication | 0.037 | 0.018 |
| Open science | 0.015 | 0.015 |
| Research integrity | 0.023 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".