Individual understandings, perceptions, and engagement with climate change: insights from in‐depth studies across the world
Bibliographic record
Abstract
Abstract Public understandings and perceptions of, as well as engagement with, climate change have garnered the interest of research and policy for almost three decades. A portion of this growing body of literature examines such perceptions in‐depth, using largely qualitative methodologies, such as personal interviews, limited sample size surveys, focus groups, and case studies. This area of research has been conducted on different continents, with individuals of different cultural backgrounds and ethnic groups, and a variety of demographic characteristics. It has examined various aspects of the communication process, such as audience differences, influence of framing, messages and messengers, information processing, etc.). This paper focuses on this subset of the climate change literature, highlighting similarities and differences across cultural, social, and geographical landscapes. Apart from demographic and regional differences, this literature also offers more detailed insights into the effectiveness of different communication strategies and into the cognitive and psychological processes that underlie public opinions. These insights are generally not obtained through large‐scale opinion surveys. Our review highlights great variation and sometimes direct contradiction between these pieces of research. This not only points to a need for further refinement in our knowledge of public understanding and engagement, but also simply to accept that no one theory will explain the variation in human experience of climate change and action in response to it. WIREs Clim Change 2011 2 547–569 DOI: 10.1002/wcc.120 This article is categorized under: Perceptions, Behavior, and Communication of Climate Change > Perceptions of Climate Change
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".