Elite Framing and Carbon Pricing: An Experiment in Embracing Expert Consensus
Bibliographic record
Abstract
Can conservatives be persuaded to support carbon pricing?This thesis leverages a policy pivot by the Conservative Party of Canada and asks if elite framing moved public sentiment towards a climate policy backed by expert consensus.Using an original survey experiment deployed throughout the 2021 federal election campaign, I test if the Conservative Leader's genuine endorsement of carbon pricing as cost-effective increased policy understanding and support levels.Results show limited ability for conservative partisans to be cued towards carbon pricing by their party leader and even a minor backfire among out-group respondents. RésuméLes conservateurs peuvent-ils être persuadés de soutenir la tarification du carbone ?Cette thèse exploite un pivot politique du Parti conservateur du Canada et demande si le cadrage utilisé par l'élite a déplacé le sentiment public vers une politique climatique soutenue par des experts consensus.À l'aide d'une enquête originale déployée tout au long de la campagne électorale fédérale de 2021, je teste si la déclaration réelle du chef conservateur soutenant la tarification du carbone est liée à une meilleure compréhension des politiques et niveaux de soutien pour la politique.Les résultats montrent une capacité limitée des partisans conservateurs à être dirigé vers la tarification du carbone par leur chef de parti et même un retour de flamme mineur parmi les répondants hors groupe.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".