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Record W4381550716 · doi:10.1007/978-3-031-25689-9_3

Failure Where Alignment is Lacking: Type 26 Frigate Procurement Processes in Australia Versus Canada, 1990–2022

2023· book-chapter· en· W4381550716 on OpenAlexaffabout
Andrea Migone, Alexander Howlett, Michael Howlett

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsSimon Fraser UniversityToronto Metropolitan University
Fundersnot available
KeywordsProcurementDoctrineGovernment (linguistics)Government procurementBusinessMilitary doctrineOperations managementPublic administrationEngineeringPolitical scienceOperations researchLawMarketingPhilosophy

Abstract

fetched live from OpenAlex

Abstract In this chapter we take a broader look at large-scale naval procurement than is usually done by defence or policy analysis in isolation, focussing on the planned purchases of the British BAE Systems’Type 26frigate by both Canada and Australia. We argue that successful procurement/implementation in naval procurement in general requires the existence of (1) a clear naval doctrine that supports a rational for the procurement of a particular weapon system and (2) the acceptance on the part of the government of that doctrine along with a commitment to ensuring strategic alignment with it. If these two areas are aligned, as they were in Australia, procurement should proceed relatively smoothly, but issues can emerge if the doctrine is missing or unclear and/or if the government disagrees with the doctrine put forward by the military, and prioritizes purchases in other areas or services, as occurred in Canada.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.668

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0090.004
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.103
GPT teacher head0.262
Teacher spread0.159 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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