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Record W4409965082 · doi:10.1177/0095327x251331545

Officers and Civilians: A Civil–Military Gap in Canadian National Security? A Research Note

2025· article· en· W4409965082 on OpenAlexaffabout
Holly Ann Garnett, Christian Leuprecht, Sofia Caal-Lam

Bibliographic record

VenueArmed Forces & Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsPolitical scienceCivil–military relationsNational securityComputer securityCivil defensePublic administrationCriminologyLawPsychologyPoliticsComputer science

Abstract

fetched live from OpenAlex

This research note measures the political attitudes held by Canadian Military Colleges (CMC) graduates, as compared with the general population on issues related to Canadian democratic life. It employs survey data from a sample of over 1000 alumni of CMCs, complemented by data on the general population from the 2021 Canadian Election Study. The results show that CMC graduates tend to be more interested in politics and have higher levels of political efficacy than a comparable sample of civilians. However, they are no more satisfied with democracy in Canada. They tend to favor personal, rather than institutional responsibility, and tend to be slightly more right-leaning than their peers. These results show some differences between the military population and the Canadian population, although the differential is insufficient for it to have a material bearing on civil–military relations in Canada. CMC graduates are neither alienated from nor dismissive of Canadian society.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0110.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.044
GPT teacher head0.374
Teacher spread0.329 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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