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Record W2010768344 · doi:10.1177/0020702015573521

Coping with fallout: The influence of radioactive fallout on Canadian decision-making on the Distant Early Warning (DEW) Line

2015· article· en· W2010768344 on OpenAlexaffabout
Matthew Trudgen

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsOpposition (politics)Radioactive falloutPolitical scienceNuclear weaponPoliticsSoviet unionSovereigntyTreatyTerrorismLawPublic administrationEngineering

Abstract

fetched live from OpenAlex

During the 1950s, Canada and the United States worked together to develop a North American air defence system. While the military cooperation generally worked well, some difficulties did occur. These problems in the relationship were almost always the result of concerns from within the Canadian government and the Department of External Affairs that the air defence measures posed political problems and were a threat to Canada’s sovereignty. In the fall of 1954, opposition to further improvements to the continental air defence system emerged from a different source: General Charles Foulkes, the chairman of the Chiefs of Staff Committee. He believed that radioactive fallout from ground bursts of thermonuclear weapons in a war with the Soviet Union would pose insurmountable problems for the air defence effort. Thus, the countries needed to conduct a joint reappraisal of the air defence problem. This article will explore Foulkes’ position and examine what lessons can be drawn from this experience.

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)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0200.006
Scholarly communication0.0070.002
Open science0.0030.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.326
Teacher spread0.310 · 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 designQualitative
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

Citations0
Published2015
Admission routes2
Has abstractyes

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