Six Canadas of Climate Change: Segmenting Canadian Views on Anthropogenic Climate Change
Bibliographic record
Abstract
There is little doubt within the scientific community about the need for immediate action to reduce the magnitude and impacts of Anthropogenic Climate Change (ACC). To reduce carbon and other greenhouse gas emissions effective climate solutions will require the engagement and collective action of millions of people and thousands of organizations in the United States and other countries including Canada. Unfortunately, the urgency understood and felt in the scientific community has not translated to widespread pro-environmental action from the public at large, or in adequate government policy to mitigate climate change. Effective and targeted engagement strategies to improve pro-environmental behaviors remain a challenge for policy makers and communicators. \n \nThis study applies a segmentation methodology developed for the United States (Maibach, Lesierowitz, Roser-Renouf & Mertz. 2011a) to a nationally representative Canadian audience. The segmentation places Canadians into six distinct groups, the “Six Canadas of Climate Change,” based on their beliefs, motivations and policy preferences around ACC. Segmentation is a methodology borrowed from other social sciences to divide populations into distinct groups homogenous with respect to certain attributes such as beliefs, behaviors and ideology (Maibach et al., 2011a). Having identified segments allows communicators to target specific and meaningful communications targeted to groups whose beliefs, preferences and motivations are known. \n \nThe utility of this climate change segmentation tool is assessed by measuring its ability to predict respondent’s willingness to support a series of greenhouse gas (GHG) reduction policies. Linear regression models are used to assess demographic variables, political views and the segmentation as predictors of GHG mitigating policy support. All of these variables are to some degree predictive, but the segmentation best explains variation in policy preferences. \n \nThere are significant differences in views on ACC between the United States and Canada. This study offers analysis of those differences and opportunities for future research to improve and target climate communications to distinct audience segments.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".