The Rhetoric of Climate Change Communications: An Analysis of Articles from the New York Times and Fox News over a 25 Year Period
Bibliographic record
Abstract
This paper is a rhetorical content analysis of the use of certain rhetorical devices (those being imagery, personification, congeries, metaphor and simile, conceptual metaphors, and allusion) by the New York Times and Fox News at five year increments over a 25 year period between the years of 1994 and 2019. The paper seeks to answer the following questions: Which rhetorical devices do the media use to communicate information about climate change? How have the rhetorical devices changed over time (since the advent of the internet to today)? How do rhetorical devices differ between publications with different political leanings (and therefore with different methods of framing information), and by extension, between those with different approaches to writing about climate change? This paper finds that imagery visualizes abstract data or depicts natural beauty, personification portrays the natural world as both a victim and an aggressor, congeries convey a multitude of weather chaos, metaphor and simile are used to explain scientific concepts, conceptual metaphors depict climate change as a war between humans and the natural world, and allusions are used for making connections, for emotional effect, for putting the climate situation into a historic perspective.
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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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".