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Record W2626615296

Stumbling into Design: Action Experiments in Professional Military Education at Canadian Forces College

2017· article· en· W2626615296 on OpenAlexvenueaboutno aff
Paul Mitchell

Bibliographic record

VenueJournal of military and strategic studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceConstruct (python library)Action (physics)Public administrationInstitutionPublic relationsPower (physics)State (computer science)National securityCivil servantsManagementLawPoliticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Operations for small militaries are largely about aid to civil power within their home states and what has been termed “contribution warfare” in Canada. The War College is meant to teach the leaders of armed forces (and increasingly, public servants from security related departments) the knowledge necessary to “lead the institution”. In terms of strategy and operations, it is all about linking policy to military operations through the “ends/ways/means” construct of modern strategic theory. In small militaries, however, operational design is generally the purview of the leading coalition partner (typically the US, though sometimes NATO), and the policies adopted by the state may often have relatively little to do with achieving specific military objectives. Despite this fundamental epistemic challenge, well-educated military leaders have never been more important in the charged media environment in which contemporary operations are conducted. So how should senior officers be taught at the highest levels? This article examines the experience of working through this specific pedagogical challenge through the lens of “Design thinking”. It explores the origin and development of the Canadian Forces College’s “Modern Comprehensive Operations and Campaign Design” course taught to colonels and senior public servants on the National Security Programme.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
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 head0.159
GPT teacher head0.411
Teacher spread0.252 · 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 designQualitative
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

Citations4
Published2017
Admission routes2
Has abstractyes

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