Problem Uncertainty, Institutional Insularity, and Modes of Learning in Canadian Provincial Hydraulic Fracturing Regulation
Bibliographic record
Abstract
Abstract This study uses policy learning frameworks to explain variation in processes of hydraulic fracturing regulatory development in Canadian provinces. Using a cross‐case comparison of British Columbia and Nova Scotia, the article demonstrates that differences in problem uncertainty and institutional insularity in each province determined modes of technical, social, and political learning in each province. In British Columbia elected officials framed LNG as a safe, clean energy source generating economic benefits. These frames made it difficult for anti‐fracking advocates to increase the salience of environmental risks and scientific uncertainty. Low problem uncertainty and high institutional insularity fostered processes of technical learning within the BC Oil and Gas Commission focused on single‐issue regulations. In Nova Scotia, an external review provided an ad hoc institutional venue through which environmental advocates, residents, and experts could increase the salience of scientific uncertainty and dread environmental risks. These conditions fostered collective processes of social learning among anti‐fracking advocates and political learning among elected officials, resulting in a ban.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".