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Record W4415748721 · doi:10.1177/00207020251393880

Leveraging history to reach <i>Quantum 2030</i> goals: What policy makers need to know about the nuclear reactor at Chalk River, Ontario

2025· article· en· W4415748721 on OpenAlexaffabout
J. Archibald

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Science and Diplomacy
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeverage (statistics)Need to knowNuclear energy policyField (mathematics)Nuclear technologyNuclear weapon

Abstract

fetched live from OpenAlex

Canada's Quantum 2030 policy document— Quantum 2030 —outlines the Canadian Government's path toward quantum science and technology (S&amp;T) integration and places an emphasis on not completing these tasks alone. While not shocking to most policy experts, the importance of collaboration with international allies is something Canada has a long history of getting right—until very recently. This article argues that policy makers of today who want to best leverage international relationships in the field of quantum S&amp;T should look to the past for examples of when this was done well. This article will use the nuclear reactor site in Chalk River, Ontario as a case study, and will highlight how the strength of the Canada-US relationship led to robust S&amp;T innovation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.539
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.327
Teacher spread0.311 · 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 teacher head, 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

Citations0
Published2025
Admission routes2
Has abstractyes

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