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Record W4360602916 · doi:10.1017/9781108555654.021

Cold War Environmental Knowledge in the Polar Regions

2023· book-chapter· en· W4360602916 on OpenAlexaboutno aff
Stephen Bocking, Pey-Yi Chu

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsArcticGeographyPolarContext (archaeology)Ice sheetGlobal warmingCold warPermafrostPlanetClimate changeOceanographyEarth sciencePhysical geographyGeologyPolitical sciencePoliticsArchaeologyLawAstronomy

Abstract

fetched live from OpenAlex

In the twenty-first century, scientists and the media track the health of the planet in the polar regions. Shrinking sea ice in the Arctic and Southern oceans, melting ice shelves in Nunavut, Greenland, and West Antarctica, and thawing permafrost in Alaska and Siberia are signals of global warming. This has led people to refer to these regions as a ‘canary in the coal mine’ or the ‘ground zero’ and ‘epicentre’ of climate change. 1 Such metaphors impart an abstract quality to the polar regions, with rising temperatures and loss of ice serving merely as numbers on the dashboard of Spaceship Earth. This view of the polar regions as integral yet neutral – transparent indicators of a global system – has an intellectual and political history. Created by the scientific context of the Cold War, it provides a powerful, panoptic perspective of the planet while obscuring the heterogeneity and pluralism of beings and places.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.011
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.052
GPT teacher head0.273
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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