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Record W2505010416 · doi:10.1057/9781137011695_5

The Silence of the Polar Bears: Performing (Climate) Change in the Theater of Species

2012· book-chapter· en· W2505010416 on OpenAlexaboutno aff
Una Chaudhuri

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal warmingGlobeSilenceIce capsArcticSea iceThe arcticClimate changeHistoryGeographyOceanographyMeteorologyPhysical geographyGeologyArtGlacierPsychology

Abstract

fetched live from OpenAlex

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).KeywordsGlobal WarmingRock WallPolar BearGlass WallElectronic WasteThese 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.232
GPT teacher head0.354
Teacher spread0.122 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2012
Admission routes1
Has abstractyes

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