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Record W4385455396 · doi:10.1080/10242694.2023.2228565

NORAD Modernization: Private Benefits to Canada

2023· article· en· W4385455396 on OpenAlexaffabout
Ugurhan G. Berkok, Oana Secrieru

Bibliographic record

VenueDefence and Peace Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsRivalryModernization theoryNavyInterdependenceAllianceProcurementPrivate sectorConstructiveBusinessEconomicsIndustrial organizationEconomic policyInternational tradeEngineeringPolitical scienceMarketingEconomic growthLawComputer science

Abstract

fetched live from OpenAlex

The workhorse of military alliances theory is the joint products model where the prominent existence of private benefits from alliance activities alleviates the free-riding problem. In the case of NORAD Modernization project, there are potentially large private economic benefits accruing to Canada. These benefits may include technology transfers and domestically produced inputs from some sectors exhibiting comparative advantage. In this latter case, the benefits will largely depend on whether a JSF type consortium will undertake the investments efficiently as opposed to the so-called benefits obtainable from Canada’s fundamentally inefficient offsets program, Industrial and Technological Benefits (ITB). The article focuses on Canada’s Key Industrial Capabilities (KIC), the 17 sectors officially selected as supporting the country’s operational capabilities. The width of this selection as well as the procurement and industrial policy interaction are briefly discussed.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.088
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0000.002
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 head0.042
GPT teacher head0.205
Teacher spread0.163 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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