Emotional turmoil: The psycho‐social uncertainty and sensemaking challenges of climate action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change generates considerable uncertainty about impacts, vulnerability, and broad‐scale change to society. While scientific consensus is well established, social consensus—on what the issues are, what it means for nations, communities, and every life, and what we ought to do about it as a society and civilization—is harder to achieve. This “human” layer of the climate challenge is crucial yet seldom sufficiently integrated into climate action. Publics can struggle to connect their climate concern with support for climate action, while others exhibit fear, resistance, forms of denial, and pushback against climate policies. This occurs against a cultural backdrop of increasing polarization, particularly in pluralistic liberal democracies like Canada. This paper presents qualitative research in the Canadian context in which climate actors across sectors examined the current sensemaking challenges regarding climate change. Sensemaking refers to how people mentally, emotionally, cognitively, and socially construct an understanding of the world. Respondents discussed key psycho‐social and sensemaking challenges regarding climate action, many of which pertain to the emotions provoked around perceptions of a ‘just transition,’ such as feeling left behind by unfair distribution of benefits from climate action investments and feeling frustration, lack of autonomy, and loss of agency regarding changes in identity and livelihoods due to climate action. The paper submits that a better understanding of these psycho‐social and sensemaking challenges—specifically, those involving affect, emotions, worldviews, and identity—could help generate agency, uptake, and social support of climate practices and policies, as well as lessen the divides between anti‐ and pro‐climate action views.
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 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 it