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Record W4285796404 · doi:10.1177/00207020221115442

The origins and early history of Canada’s Cold War scientific intelligence, 1946-65

2022· article· en· W4285796404 on OpenAlexaffabout
Matthew S. Wiseman

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicTwentieth Century Scientific Developments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAdversaryCold warPolitical scienceFraming (construction)PreparednessMilitary scienceGovernment (linguistics)World War IILawMilitary historyHistoryPoliticsComputer security

Abstract

fetched live from OpenAlex

In the process of creating the policies and structures that led to the formal organization of Canada’s Defence Research Board after the end of the Second World War, senior military and defence officials in Ottawa conceptualized and established a scientific intelligence bureau within the defence department. Recognizing the heightened military significance of science during the war, defence officials believed that scientific intelligence—the practice of analyzing scientific information for forecasting the weapons and warfare potential of enemy countries—could support and improve Canada’s military preparedness efforts in the immediate postwar period. Using recently opened government and military records, this article explores the origins and history of Canadian scientific intelligence during the early Cold War, framing the topic as useful for understanding Canada’s military past and Ottawa’s approach to some of the country’s top security and defence issues of the late 1940s through the mid-1960s.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.011
Science and technology studies0.0290.023
Scholarly communication0.0120.003
Open science0.0020.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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