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Record W1966064457 · doi:10.1177/1548512912466195

Risk-analytic approaches to the allocation of defence operating funds

2012· article· en· W1966064457 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Defense Modeling and Simulation Applications Methodology Technology · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsRoyal Military College of CanadaRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsKnapsack problemContext (archaeology)Order (exchange)Rank (graph theory)Computer scienceOperations researchInteger (computer science)Value (mathematics)Risk analysis (engineering)BusinessFinanceEngineeringMathematicsOperating system

Abstract

fetched live from OpenAlex

We develop two risk-analytic approaches for allocating operating funding among defence organization activities. In one, termed the priority method, activities are put in rank order and as many high-priority activities as possible are undertaken while ensuring that the budget holder’s probability of overspending his budget is acceptably small. In the second, termed the knapsack method, there are two kinds of activities: must-do activities and optional activities. Optional activities are selected using a nonlinear integer program that maximizes the value of the optional activities while keeping the probability of overspending sufficiently low. Both approaches are applied in the context of the Department of National Defence 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.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.366
GPT teacher head0.343
Teacher spread0.023 · 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