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Record W4416294472 · doi:10.1177/00207020251397653

Military mobilization and nation-building: Aligning defence investment with economic development

2025· article· en· W4416294472 on OpenAlexaffabout
Daniel Cere, Lt. Nikolas Dolmat

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsRoyal Canadian NavyMcGill University
Fundersnot available
KeywordsGeopoliticsInvestment (military)Economic nationalismNational securityMilitary threatMobilizationIndustrialisationEconomic recoveryNationalism

Abstract

fetched live from OpenAlex

Canada faces pressing challenges that threaten its economic stability, national security, and military readiness. Growing American nationalism and isolationism heighten the dangers of Canada's over-dependence on the United States, while economic reconstruction, a housing crisis, and increasing geopolitical threats in the North and Arctic demand urgent attention. Despite mounting international pressure, Canada's military investment remains insufficient even for its most basic necessities. Canada's Auditor General recently reported that the military itself is facing its own housing crisis, with aging and deteriorating facilities and thousands of military members without residential housing. Can there be an integrated national strategy that aligns military expansion with economic development? At critical moments in history, military mobilization has driven economic transformation in Canada, and this study proposes leveraging Canada's military to address the national housing crisis.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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 designTheoretical or conceptual
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
Published2025
Admission routes2
Has abstractyes

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