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Record W2901022471 · doi:10.22215/etd/2018-12641

Executive (In) Decision? Explaining Delays in Canada's Defence Procurement System, 2006-2015

2018· dissertation· en· W2901022471 on OpenAlexafffundabout
Jeffrey F. Collins

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsCarleton UniversityUniversité du Québec en Outaouais
FundersMinistère de la Défense NationalePublic Works and Government Services CanadaQueen's UniversityIndustry CanadaGovernment of CanadaStrongCanadian Armed ForcesInnovation, Science and Economic Development Canada
KeywordsProcurementCabinet (room)PoliticsPublic administrationBureaucracyGovernment (linguistics)Political scienceEngineeringOperations managementManagementLawEconomics

Abstract

fetched live from OpenAlex

This dissertation asks how delays in Canada's defence procurement system can be explained. In answering this question, the hypothesis tested is that of the 'political executive'; the political body composed of the prime minister, cabinet and their advisors who sit at the apex of the federal government. With final decision-making powers over defence policy and budgets, the political executive has been inferred in existing scholarship as a decisive factor in delaying Major Crown Projects (MCPs) from moving through the procurement process but this has never been the subject to a scholarly analysis. Three other independent variables commonly identified in the literature as causing procurement delays were tested alongside the political executive: (1) the defence procurement bureaucracy; (2) the defence industry; (3) and Canada's military alliances and involvement in the Afghanistan war (2001-2014). Delays are treated as the dependent variable and are defined as a MCP not meeting its original planned project milestone dates.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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 teacher head, not a consensus.

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

Citations8
Published2018
Admission routes3
Has abstractyes

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