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Frontier engineering: from the globe to the body in the Cold War Arctic

2006· article· en· W2022765030 on OpenAlexafffundvenueabout
Matthew Farish

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsMemorial University of Newfoundland
FundersUniversity of TorontoUniversity of British ColumbiaArctic Institute of North AmericaSmithsonian Institution
KeywordsFrontierGeopoliticsArcticPoliticsPolitical scienceWildernessCold warNarrativeGlobeSovereigntyNatural resourceEnvironmental ethicsGeographyLawOceanographyEcologyGeologyPsychology

Abstract

fetched live from OpenAlex

The dual themes of sovereignty and wilderness have come to define, or at least dominate, historical discussions of the North American Arctic. This paper argues that neither adequately captures the role of the Arctic during the early Cold War, a period of unprecedented interest in northern landscapes. Political and environmental approaches, with their national undertones, were incorporated into a dominant narrative whose implications were far less abstract: the Arctic became a frontier for military science, both imaginatively and materially. Civilian institutions with military affiliations emerged to advocate for additional Arctic research in the natural and social sciences, whereas Canadian and American military agencies established laboratories and training centres, constructed complex defence networks and staged numerous military exercises across Arctic spaces, operations which tested the performance of both humans and machines. These projects actively engineered Arctic terrain in the name of scholarly advancement and military necessity. If the Cold War Arctic is to be understood geographically, then the national scale must be placed next to the broader views of geopolitics and scientific inquiry but also next to the finer perspectives of military bodies moving across ‘hostile’ terrain.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.204
Teacher spread0.198 · 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.

Study designNot applicable
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

Citations73
Published2006
Admission routes4
Has abstractyes

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