Purgatory islands and climate death-worlds: Interrogating the journalistic imperative to witness the climate crisis through the lens of war
Bibliographic record
Abstract
In this article, I examine and critique how the current and predicted future impacts of climate change are often reported on through the aesthetics and discourse of war. I argue that the journalistic imperative to witness climate change is important to consider here. Indeed, news images and descriptive accounts of climate change are often privileged for their evidentiary value according to a very strict set of visual criteria shaped by an established definition of what violence and war look like. Through a multimodal analysis of news coverage of the aftermath of Hurricane María across prominent US news magazines, I examine what constitutes compelling evidence of climate change, why and to what end in terms of the types of responses featured and proposed by journalists. Ultimately, my analysis reveals how Puerto Rico is demarcated as a ‘death-world’ across publications, effectively casting Puerto Rico as a ‘purgatory island’ dependent on the help of the United States represented as a saviour.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".