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Record W4414297010 · doi:10.1038/s44168-025-00290-x

The effect of public trust and engagement on climate communication approaches

2025· article· en· W4414297010 on OpenAlexafffundabout
Ashley Rose Mehlenbacher, Sara Doody, Roy Brouwer, Justin Steinburg, Carolyn Eckert, Sarah Forst, Chris Kampe, Brad Mehlenbacher

Bibliographic record

Venuenpj Climate Action · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaMitacsCanada Research Chairs
KeywordsPerceptionClimate changePerspective (graphical)TrustworthinessAction (physics)Public engagementPublic trustPublic opinionRisk communication

Abstract

fetched live from OpenAlex

The urgency of the climate crisis requires global action, which must account for regional interests and realities. We offer a Canadian perspective by reporting the results of a national survey investigating Canadians’ perceptions and beliefs about climate change and who they trust and engage with on matters concerning the climate crisis. We find that while the majority of Canadians believe in anthropogenic climate change, there is not always agreement over who is a trustworthy authority on climate change, nor is there always a willingness to engage in conversations. Our findings suggest that demographic segmentation is not an especially powerful guide for crafting communication approaches, but that probing participants’ experience and success in climate conversations and their perception of authorities on climate change provides important insights. Findings are informative both for those hoping to spur climate action through effective communication in Canada, and beyond, through attention to individuals’ communicative resources.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.704

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.118
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0100.005
Open science0.0010.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.563
GPT teacher head0.471
Teacher spread0.091 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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