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Record W4385638189 · doi:10.32920/23657004.v1

Procurement and Politics: Strategies of Defence Acquisition in Canada and Australia

2023· preprint· en· W4385638189 on OpenAlexafffundabout
Andrea Migone, Alexander Howlett, Michael Howlett

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsToronto Metropolitan UniversitySimon Fraser UniversityUniversity of Calgary
FundersCanadian Armed ForcesQueen's UniversityMcGill University
KeywordsProcurementAppealAdministration (probate law)PoliticsPublic administrationGovernment (linguistics)Political scienceScale (ratio)BusinessStrategic defencePublic relationsLawMilitary scienceMarketing

Abstract

fetched live from OpenAlex

This open access book compares the experiences of large-scale military procurement in Canada and Australia. Focusing on the recent frigate and jet-fighter programmes, it demonstrates how delays suffered in delivering weapons systems and platforms in these countries have been caused by misalignments between the strategic requirements set out by the armed forces and government defence policies. By bringing the insights of public management and administration to those of defence studies, the book presents policy options that will help improve the nature of future large-project military procurement. It will appeal to scholars and students of public administration, public management, and defence studies, as well as practitioners and policymakers.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.163
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.007
Scholarly communication0.0100.002
Open science0.0010.003
Research integrity0.0020.002
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 head0.092
GPT teacher head0.272
Teacher spread0.180 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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