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Record W2118078063 · doi:10.7202/1006149ar

Exploring Greenland: Science and Technology in Cold War Settings

2011· article· en· W2118078063 on OpenAlexaffvenue
Matthias Heymann, Henrik Nellemose Knudsen, Maiken L. Lolck, Henry Nielsen, Kristian Hvidtfelt Nielsen, Christopher Jacob Ries

Bibliographic record

VenueScientia Canadensis Canadian Journal of the History of Science Technology and Medicine · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGeopoliticsCold warSovereigntyPolitical scienceArcticGeographyInternationalism (politics)NationalismDanishOceanographyEconomic historyHistoryLawGeologyPolitics

Abstract

fetched live from OpenAlex

This paper explores a vacant spot in the Cold War history of science: the development of research activities in the physical environmental sciences and in nuclear science and technology in Greenland. In the post-war period, scientific exploration of the polar areas became a strategically important element in American and Soviet defence policy. Particularly geophysical fields like meteorology, geology, seismology, oceanography, and others profited greatly from military interest. While Denmark maintained formal sovereignty over Greenland, research activities were strongly dominated by U.S. military interests. This paper sets out to summarize the limited current state of knowledge about activities in the environmental physical sciences in Greenland and their entanglement with military, geopolitical, and colonial interests of both the USA and Denmark. We describe geophysical research in the Cold War in Greenland as a multidimensional colonial endeavour. In a period of decolonization after World War II, Greenland, being a Danish colony, became additionally colonized by the American military. Concurrently, in a period of emerging scientific internationalism, the U.S. military "colonized" geophysical research in the Arctic, which increasingly became subject to military directions, culture, and rules.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0010.057
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.035
GPT teacher head0.210
Teacher spread0.175 · 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 designObservational
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

Citations35
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueScientia Canadensis Canadian Journal of the History of Science Technology and MedicineSame topicPolar Research and EcologyFrench-language works237,207