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Record W2301956550 · doi:10.55016/ojs/sppp.v6i1.42451

Canadian Defence Commitments: Overview and Status of Selected Acquisitions and Initiatives

2013· article· en· W2301956550 on OpenAlexaffabout
Elinor Sloan

Bibliographic record

VenueThe School of Public Policy Publications · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolitical scienceEnvironmental planningGeography

Abstract

fetched live from OpenAlex

For years, successive Canadian governments have been overpromising and under-delivering on defence procurement. Timetables have slipped even as repair and maintenance costs for aging equipment have soared, while elaborate rules have obscured the acquisition process in a bureaucratic fog. This paper assembles information from a wide range of official sources and cuts through the confusion. It surveys 15 Canadian defence acquisitions and initiatives, each anticipated to cost more than $100 million, to account for the delays. Final replacements for the ancient Sea King helicopters are no closer to arriving — after almost 30 years — because the DND failed to recognize that it asked for technology that is still in development. The Joint Support Ship project is years behind schedule because, as originally conceived, it sought to integrate so many capabilities that it was unbuildable. The Integrated Soldier System Project is almost as far behind because Ottawa’s procurement rules are so complex and niggling that no bidder could fulfill every single one. Canada faces evolving threats, but efforts to equip the Canadian Forces to meet them have been marked by a long litany of failures — failures of communication, of organization and of vision. This paper sets out the military procurement process, and concisely explains the most egregious flaws, making it essential reading for anyone interested in the future of Canada’s military.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.714
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.280
Teacher spread0.217 · 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.

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

Citations2
Published2013
Admission routes2
Has abstractyes

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