Bibliographic record
Abstract
Abstract Climate change, which includes global warming, is a serious and pervasive challenge for local and global communities. Communication theorists, researchers, and practitioners are well positioned to describe, predict, and affect how we communicate about climate change. Our theories, research methods, and practices have many potential roles in reducing climate change and its effects. Climate change communication is a growing field that examines a range of factors that affect and are affected by how we communicate about climate change. Climate change communication covers a broad range of philosophical and research traditions, including humanistic-rhetorical analyses, interpretive qualitative studies, and social-scientific quantitative surveys and experiments. Climate change communication examines a range of factors that affect and are affected by how we communicate about climate change. Much of the research in climate change communication focuses on public understanding of climate change, factors that affect public understanding, media coverage and framing, media effects, and risk perceptions. Less prevalent, growing areas of research include civic engagement and public participation, organizational communication, and persuasive strategies to affect attitudes, beliefs, and behaviors related to the climate. In all of these areas, most of the research on climate change communication has been conducted in the United States, United Kingdom, Australia, Canada, and Western European countries. There is a need to expand the climate change communication research into other regions, particularly developing countries. In addition, climate change communication has natural links to environmental and health communication; therefore, communication scholars should also examine research from these areas to develop insights into climate change communication.
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 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.026 |
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".