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Record W2970431734 · doi:10.3138/chr.2018-0103

Canadian Scientists and Military Research in the Cold War, 1947–60

2019· article· en· W2970431734 on OpenAlexvenueaboutno aff
Matthew S. Wiseman

Bibliographic record

VenueCanadian Historical Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMilitarizationPolitical scienceGovernment (linguistics)Military scienceContext (archaeology)National securityPoliticsPublic administrationCold warMilitary sociologyLawMilitary operations other than warSpanish Civil WarHistory

Abstract

fetched live from OpenAlex

This article examines the militarization of academic science in Canada during the Cold War period between 1947 and 1960. As evidenced by the extramural program of the Defence Research Board (drb), the scientific research branch of the Canadian armed services, funding for defence attracted faculty and graduate students to the world of military research. Government officials and civilian scientists collaborated to establish and grow the drb’s extramural program, championing military research at Canadian universities and shaping academic work in both the physical and social sciences. Academics across the country benefited from the autonomy drb scientists held over the direction and distribution of federal funds made available for military research, suggesting that the militarization of academic science in Canada came from within the existing scientific community. An assessment of the political and financial ties established among government authorities and university scientists reveals the extent to which national security concerns influenced the work of some of Canada’s top academics. Military sponsorship is an understudied yet important object of theory and analysis for historians of Canada and the Cold War, and it is incumbent upon Canadian historians to discuss and debate the role and influence of military research in the university context.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.072
GPT teacher head0.273
Teacher spread0.201 · 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
GenreReview

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

Citations3
Published2019
Admission routes2
Has abstractyes

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