The Relationships Between Climate Change News Coverage, Policy Debate, and Societal Decisions
Bibliographic record
Abstract
Abstract This article considers the relationship between news media and the sociopolitical dimensions of climate change. Media can be seen as sites where various actors contend with one another for visibility, for power, and for the opportunity to communicate, as well as where they promote their policy preferences. In the context of climate change, actors include politicians, social movement representatives, scientists, business leaders, and celebrities—to name a few. The general public obtain much of their information about climate change and other environmental issues from the media, either directly or indirectly through sources like social media. Media have their own internal logic, and getting one’s message into the media is not straightforward. A variety of factors influence what gets into the media, including media practices, and research shows that media matter in influencing public opinion. A variety of media practices affect reporting on climate change─one example is the journalistic norm of balance, which directs that actors on both sides of a controversy be given relatively equal attention by media outlets. In the context of global warming and climate change, in the United States, this norm has led to the distortion of the public’s understanding of these processes. Researchers have found that, in the scientific literature, there is a very strong consensus among scientists that human-caused (anthropogenic) climate change is happening. Yet media in the United States often portray the issue as a heated debate between two equal sides. Subscription to, and readership of, print newspapers have declined among the general public; nevertheless, particular newspapers continue to be important. Despite the decline of traditional media, politicians, academics, NGO leaders, business leaders, policymakers, and other opinion leaders continue to consume the media. Furthermore, articles from particular outlets have significant readership via new media access points, such as Facebook and Twitter. An important concept in the communication literature is the notion of framing. “Frames” are the interpretive schemas individuals use to perceive, identify, and label events in the world. Social movements have been important actors in discourse about climate change policy and in mobilizing the public to pressure governments to act. Social movements play a particularly important role in framing issues and in influencing public opinion. In the United States, the climate change denial countermovement, which has strong links to conservative think tanks, has been particularly influential. This countermovement is much more influential in the United States than in other countries. The power of the movement has been a barrier to the federal government taking significant policy action on climate change in the United States and has had consequences for international agreements and processes.
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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 | 0.009 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".