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Record W4402934410 · doi:10.1080/11926422.2024.2397970

Canada’s national quantum strategy one year on

2024· article· en· W4402934410 on OpenAlexafffundabout
Kristen Csenkey, Aniska Graver

Bibliographic record

VenueCanadian Foreign Policy Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Science and Diplomacy
Canadian institutionsWilfrid Laurier UniversityProfessional Engineers OntarioBalsillie School of International Affairs
FundersMinistère de la Défense NationaleMinistère de la DéfenseCanadian Armed Forces
KeywordsPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

This policy commentary critically reviews Canada's National Quantum Strategy (NQS) to evaluate whether it integrates a values approach to mitigate any potential regional, national, and global disparities as part of a responsible quantum approach. Our analysis reveals that Canada's quantum strategy focuses on fostering the identification, development, and commercialization of technologies for economic gain at national, regional, and international levels. Both provincial and federal governments actively collaborate with research institutions and businesses within four distinct research and innovation hubs to establish “quantum zones” as emerging paradiplomatic actors. Internationally, Canada is positioning itself as a leader in quantum technology through grants, research and development partnerships, and country-specific Trade Missions with like-minded states. This commentary highlights the need to embed values, such as sustainability and equitability, into the development and application of quantum technologies and future iterations of the NQS to address societal impacts.

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)0.011
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: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0190.008
Scholarly communication0.0130.003
Open science0.0030.003
Research integrity0.0130.009
Insufficient payload (model declined to judge)0.0200.003

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 head0.047
GPT teacher head0.350
Teacher spread0.303 · 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
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

Citations3
Published2024
Admission routes3
Has abstractyes

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