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Record W7099588004

Prepared for the Canadian Defence & Foreign Affairs Institute’s “Research Paper Series”

2005· article· en· W7099588004 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsForeign policyVariety (cybernetics)Quarter (Canadian coin)Foreign relationsSubject (documents)Prime ministerInternational relations
DOInot available

Abstract

fetched live from OpenAlex

Jean-Sébastien Rioux’s study on Québec Francophone views on Canadian foreign and defence policy is the first of a series of new research publications that will be published every quarter by the Canadian Defence & Foreign Affairs Institute (CDFAI). CDFAI is a Calgary-based “think tank ” dedicated to encouraging academic research into issues of Canadian foreign and defence policy. CDFAI’s intent is to circulate that research as widely as possible to policy-makers, community leaders, business leaders, academics, journalists and other Canadians with an interest in Canadian international and military affairs. CDFAI is a federally approved charitable organization that sponsors a variety of projects and programs dedicated to this end. The full range of CDFAI activities can be reviewed on the CDFAI website www.cdfai.org. This paper provides much food for thought and suggests additional areas of research into the little explored subject of how much of an impact Canada’s ethnic, demographic, and regional composition has on the making of Canadian foreign and defence policy. Rioux quite naturally focuses on the prime dichotomy of Canadian life. That dichotomy is based on the founding of Canada on two linguistic groups – English and French-speaking Canadians – and

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.210
Threshold uncertainty score0.539

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.001
Scholarly communication0.0070.002
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.1610.036

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 head0.051
GPT teacher head0.285
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2005
Admission routes1
Has abstractyes

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