‘Sensemaking’ climate change: navigating policy, polarization and the culture wars
Bibliographic record
Abstract
Climate action faces evolving challenges in industrialized, high-income countries, such as increased populist distrust in government institutions, growing polarization, and social contestation regarding types of climate policy. These challenges complexify timely mobilization of climate action, compromising current and future climate investment and policies. Here, we investigate the nuances of ‘sensemaking’, resistance, and polarization in regard to climate change to better understand climate-action barriers in British Columbia, Canada. Through a series of focus groups, leading climate actors from multiple sectors co-produced knowledge on current psycho-social challenges encountered when engaging publics on climate change. Findings explore how emotions about climate transitions are leveraged by disinformation messaging, obscuring an already complicated sensemaking task regarding climate change and contributing to opposition against climate policies and action. The study’s implications are relevant to climate change-related policy creation, communication, and public engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".