Climate media amidst technopolitical change: challenges, transformations, and new directions for research
Bibliographic record
Abstract
Abstract In this essay, we seek to provide a meta-level view of research on mediated climate change communication, taking stock of its achievements, historical and contemporary challenges, and future directions. While existing climate media scholarship has generated important insights to guide research and practice, recent empirical developments and technopolitical transformations challenge the traditional structure of climate media research. Historically, this research developed a tripartite structure where scholars have tended to focus on one of three distinct phases of the mediated communication process: (1) the production of narratives, frames, images, and other forms of communication about climate change; (2) the content and dissemination of these communication artifacts by and across media industries and institutions; and (3) these artifacts’ reception by and effects on policymakers, partisans, and publics. However, recent developments in communication technologies, media ecosystems, and the broader political landscape—including the increasing importance of social media and AI, new forms of climate obstruction, and rising antidemocratic forces across borders—have made these traditional lines of demarcation increasingly unworkable. While the lines of demarcation between production, dissemination, and reception are increasingly blurred in important new empirical phenomena, each has remained central in many scholarly works and the development of research questions. This persistence of the tripartite model, we argue, has caused climate media research to be slow to reflect the shifting dynamics of mediated climate communication today. After describing and analyzing the structural challenges that make doing more comprehensive climate media research so challenging, we conclude with proposals for new directions for scholarship that can help future research more fully contend with recent technopolitical transformations and move towards actionable research that is capable of grappling with and motivating robust responses to the complexities of climate change amid mounting authoritarian threats.
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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 | 0.002 | 0.001 |
| 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".