The Silence of the Polar Bears: Performing (Climate) Change in the Theater of Species
Bibliographic record
Abstract
In 2006, a single picture launched a thousand articles about global warming. It ran in the Sunday Telegraph , the New York Times , the Boston Globe , the International Herald Tribune , the Times of London , and many other papers. It was said to have been taken by Canadian environmentalists and to show a pair of polar bears stranded on Arctic ice that was shrinking due to global warming. It made polar bears the poster animals of global warming, a status they retained even after the photograph’s evidentiary status was discredited: it turned out that the photograph wasn’t taken by environmentalists, but by a student of marine biology, who did not release the image herself, and who never intended to convey that she was recording evidence of global warming. Moreover, the photo was taken in August, at the height of the Alaskan summer, when melting ice is normal. The ice floes pictured were not very far from land, and polar bears are good swimmers (Sheppard). Predictably, right-wing antienvironmentalists and global-warming deniers such as Rush Limbaugh were quick to use the episode to their own advantage, saying that this “fraud” was “a great little microcosm for the entire global warming escapade” (quoted by Zurkow). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".