Opinion poles: Polarised views on energy developments in Canada's oil province
Bibliographic record
Abstract
Energy projects are often subject to polarised opinions, with both extreme support and extreme opposition being present. This polarisation is often not accounted for in models of public preferences for energy-related issues, which limits analysts' understanding of the dynamics of public acceptance of policies or projects, and how acceptance could be enhanced. The objectives of this study are to develop an approach to test and account for polarisation in vignette experiments, and measure levels and identify drivers of polarisation. Our case studies examine hydraulic fracturing and wind energy developments in Alberta, Canada. We use a latent-class modelling approach to account for polarised or extreme groups of respondents, which we identify as two groups of respondents who exhibit extreme support for or opposition to all energy developments, regardless of its characteristics. We demonstrate that more neutral individuals are more reactive to changes in the policy attributes presented in the experiments. Trust in entities, such as government, industry, and environmental organisations, contributes to explaining class membership. Modelling the incidence of polarisation and accounting for preference heterogeneity in vignette experiment responses can offer beneficial insights to researchers and policy-makers that conduct public engagement and consultation processes. Our results help inform how energy development policy and program changes can be made towards garnering support from members of the public whose views are sensitive to the characteristics of the changes. • Polarised opinions are prevalent for wind energy and hydraulic fracturing projects • Polarised people will not change their opinion of projects in response to concessions • Trust in agencies involved with energy issues is a key driver of polarisation • A constrained latent-class model accounts for polarisation in vignette experiments • We demonstrate the value of latent-class analysis in factorial survey experiments
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".