MétaCan
Menu
Back to cohort
Record W2058450879 · doi:10.1017/s0008423914000754

Canadian Public Opinion about the Military: Assessing the Influences on Attitudes toward Defence Spending and Participation in Overseas Combat Operations

2014· article· en· W2058450879 on OpenAlexaffabout
Scott Fitzsimmons, Allan Craigie, Marc André Bodet

Bibliographic record

VenueCanadian Journal of Political Science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsUniversité LavalUniversity of British Columbia
Fundersnot available
KeywordsGovernment (linguistics)Political sciencePublic opinionMilitary personnelInstitutionPublic spendingPublic administrationPublic relationsEconomic growthEconomicsLawPolitics

Abstract

fetched live from OpenAlex

Abstract Despite more than a decade of heightened defence spending and active fighting in the War in Afghanistan, the longest combat operation in the history of the Canadian Forces, scholars know precious little about how the socio-demographic characteristics and attitudes of Canadians may influence their views about taking part in overseas combat operations and funding the institution charged with carrying out these dangerous activities. By testing a range of hypotheses, which purport to explain the influence of multiple socio-demographic and attitudinal factors on Canadians' attitudes toward defence spending and the participation of the Canadian Forces in overseas combat operations, against data from the 2004 and 2011 Canadian Election Study, this article ascertains the most important determinants of Canadians' preferences about defence spending and the use of military force by the Government of Canada.

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)0.007
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 score0.023
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.097
GPT teacher head0.337
Teacher spread0.240 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Political ScienceSame topicDefense, Military, and Policy StudiesFrench-language works237,207