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Record W184318465 · doi:10.21236/ada435827

Operational Art in a Middle-Power Context: A Canadian Perspective

2004· report· en· W184318465 on OpenAlexaboutno aff
Richard N. Dickson

Bibliographic record

Venuenot available
Typereport
Languageen
FieldHealth Professions
TopicHealthcare, Law, Governance, and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Context (archaeology)Middle powerPower (physics)Computer scienceGeographyPolitical scienceArchaeologyPhysicsArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

This monograph considers whether operational art and the operational level of conflict are viable constructs for Canada or other middle-powers. Like most of America's close allies, Canada quickly followed the US lead by adopting these operational concepts into its service and joint doctrine. However, these concepts are framed in a great-power context of large-force, large-theater, high-intensity operations that is of questionable relevance to middle-powers, and their small, tactically focused militaries. This study first examines operational doctrine and theory in order to distill operational art into terms applicable across the spectrum of conflict and scale of operations. It then explores Canadian strategic imperatives and the Canadian Army's historical experience, to determine if and how the operational art and level have been practiced in the past, and whether they are feasible, acceptable and suitable constructs for the Canadian military today. This monograph shows that in today's complex operating environment, Canada is coming under increasing pressure to take more prominent roles in coalition operations. To meet this challenge, and to ensure Canada retains the ability to exert strategic influence, the Canadian Forces need to refocus on fielding salient, self-contained forces that can think operationally and function at the operational level.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.127
GPT teacher head0.435
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2004
Admission routes1
Has abstractyes

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