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Record W4385455396 · doi:10.1080/10242694.2023.2228565

NORAD Modernization: Private Benefits to Canada

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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