Bibliographic record
Abstract
Climate communication is seemingly stuck in a double bind. The problem of global warming requires inherently trans-scalar modes of engagement, encompassing times and spaces that exceed local frames of experience and meaning. Climate media must therefore negotiate representational extremes that risk overwhelming their audience with the immensity of the problem or rendering it falsely manageable at a local scale. The task of visualizing climate is thus often torn between scales germane to the problem and scales germane to individuals. In this paper I examine how this scalar divide has been negotiated visually, focusing in particular on Ed Hawkins' 2016 viral climate spiral. To many, the graphic represents a promising union of political and scientific communication in the public sphere. However, formal analysis of the gif's reception suggest that the spiral was also a site of anxiety and negative emotion for many viewers. I take these conflicting interpretations as cause to rethink current assumptions about best practices and desirable outcomes for scalar mediations of climate and their capacities to mobilize a wide range of reactions and interpretations—some more legibly political and some more complicatedly affective, yet all nevertheless integral to the work of building a holistic response to the climate crisis.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".