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Record W3154671240 · doi:10.1177/15485129211010227

Wargaming the use of intermediate force capabilities in the gray zone

2021· article· en· W3154671240 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.

Bibliographic record

VenueThe Journal of Defense Modeling and Simulation Applications Methodology Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsComputer scienceTask forceGray (unit)Task (project management)Adversarial systemSimulationOperations researchAeronauticsAerospace engineeringSystems engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This work reviews the development and tests of an intermediate force capability (IFC) concept development hybrid wargame aimed at examining a maritime task force’s ability to counter hybrid threats in the gray zone. IFCs offer a class of response between doing nothing and using lethal force in a situation that would be politically unpalatable. Thus, the aim of the wargame is to evaluate whether IFCs can make a difference to mission success against hybrid threats in the gray zone. This wargame series was particularly important because it used traditional game mechanics in a unique and innovative way to evaluate and assess IFCs. The results of the wargame demonstrated that IFCs have a high probability of filling the gap between doing nothing and using lethal force. The presence of IFCs provided engagement time and space for the maritime task force commander. It also identified that development of robust IFC capabilities, not only against personnel, but against systems (trucks, cars, UAVs, etc.), can also effectively counter undesirable adversarial behavior

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)0.005
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.163
GPT teacher head0.387
Teacher spread0.224 · 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