Policy learning, motivated scepticism, and the politics of shale gas development in British Columbia and Quebec
Bibliographic record
Abstract
Abstract What is policy learning and how do we know when we observe it? This article develops an original way of operationalizing policy learning at the individual and subsystem level. First, it juxtaposes four types of opinion change at the individual level – opinion shifting; opinion softening; position-taking and opinion hardening. This last change, we argue is indicative of motivated scepticism, a non-learning process that we borrow from public opinion studies. Second, we identify factors associated with opinion change and argue that some of them indicate policy learning, while others point to motivated scepticism. Lastly, we examine learning and motivated scepticism against patterns of opinion convergence (the expected outcome of learning) and polarization (the expected outcome of motivated scepticism) at the subsystem level. We illustrate the use of this approach to study policy learning with the case of shale gas development in two Canadian provinces, British Columbia and Quebec. While, we find clear signs of individual learning and motivated scepticism in both provinces, we find that policy learning is more prevalent in Quebec than in British Columbia at the subsystem level.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".