Meme-ing while the world burns: Climate change news on Reddit and the cultural politics of platform participation
Bibliographic record
Abstract
This article makes a case for the value and urgency of a cultural study’s contextual approach to researching how climate change news events are interpreted by audiences. Research that applies a classically cultural studies ‘bottom-up’ approach to studying climate media audiences in the spaces and contexts where they encounter and negotiate meaning is surprisingly sparse in the scholarship on climate news reception. The article draws on a case study: a qualitative thematic analysis of Reddit discussion threads in which users responded to images and videos of New York City obscured by smoke from Canadian wildfires in June 2023. Analysis of the threads demonstrates how Reddit users drew upon popular culture references to films, television, video games and other media to articulate the strange and shocking nature of the images; expressed a range of ambivalent affective states from detachment to despair, anger and fear, but not hope, and shared knowledge and advice in the humorous, geeky, authentic and authoritative discursive and affective registers valued on the platform. Taking a contextual approach to researching climate media audiences reveals how the social and cultural context in which climate news stories or images are encountered plays a fundamental role in shaping understanding and feeling, and how this comes to underpin the kinds of climate politics and futures that are imagined as possible or impossible.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".