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Record W4381550148 · doi:10.1007/978-3-031-25689-9_2

Procuring Advanced Weapon Systems and Platforms: Four Types of Procurement Considered

2023· book-chapter· en· W4381550148 on OpenAlexaff
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
KeywordsProcurementReworkEngineeringComputer securityService (business)Risk analysis (engineering)Systems engineeringComputer scienceOperations researchOperations managementBusinessMarketing

Abstract

fetched live from OpenAlex

Abstract Major weapon systems and platforms, by nature, tend to be extremely complex and long-term purchases, incorporating cutting edge technology that is both developmentally complex and very expensive, often only being developed as the platform is built, put into service and updated, as has often been the case with new generations of fighter aircraft and warships. It is also the case with such systems that, like a hydroelectric dam or nuclear reactor, they cannot be acquired piecemeal but must be purchased and constructed all in one piece. This complexity, high costs and the lack of severability of contracts once signed are characteristics of procurement processes that generally trigger hard-fought political battles about costs, benefits and platform choices. The chapter lays out a four-fold categorization of procurement to address these complexities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.108
GPT teacher head0.234
Teacher spread0.127 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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 routes1
Has abstractyes

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