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Record W4319165437 · doi:10.1093/polsoc/puad001

The politics of military megaprojects: discursive struggles in Canadian and Australian naval shipbuilding strategies

2023· article· en· W4319165437 on OpenAlexaffabout
Andrea Migone, Alexander Howlett, Michael Howlett

Bibliographic record

VenuePolicy and Society · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsSimon Fraser UniversityToronto Metropolitan University
Fundersnot available
KeywordsMegaprojectProcurementShipbuildingPoliticsScale (ratio)BusinessCredibilityIndustrial organizationPolitical scienceEconomicsManagementMarketing

Abstract

fetched live from OpenAlex

Abstract Large-scale military platform procurement is an essential but understudied component of the policy studies of megaprojects. Procurement decisions in this area, from ships to aircraft, are examples of a specific type of often very expensive purchases which feature complex multi-actor and multiyear processes characterized by high degrees of conflict between actors over purchases and planning horizons. This study of military procurement efforts of this type demonstrates the importance of maintaining policy ‘alignment’ between governments and service providers for successful megaproject procurement to occur and suggests several strategies for accomplishing this that can be applied to similar large-scale but nondefense-related projects, ranging from hydroelectric dams to high-speed railway development.

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)0.008
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0320.029
Scholarly communication0.0160.003
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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 designQualitative
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

Citations23
Published2023
Admission routes2
Has abstractyes

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