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Record W4394577991 · doi:10.53477/2284-9378-24-01

Military culture: Understanding deeper dynamics through the warrior archetype

2024· article· en· W4394577991 on OpenAlexaffabout
Eric OUELLET

Bibliographic record

VenueBULLETIN OF CAROL I NATIONAL DEFENCE UNIVERSITY · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsCanadian Forces CollegeRoyal Military College of Canada
Fundersnot available
KeywordsArchetypeDynamics (music)HistoryArtSociologyLiterature

Abstract

fetched live from OpenAlex

This paper proposes a new psycho-sociological approach to understanding military culture change, built on the notion of warrior archetype, in line with psychiatrist Carl Jung’s concept of archetype. It contends that military culture and its related institutional forms fundamentally seek to mobilize on an ongoing basis human energy produced through the activation of the warrior archetype. The archetype is built on enhancing feelings of strength in numbers, and empowerment through socially sanctioned actions and potential use of violence. It uses the example of the Canadian Armed Forces culture change effort to illustrate that any such planned organizational culture change will fail if it does not remain consistent with activating the warrior archetype, as its central dynamic and purpose.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.031
Scholarly communication0.0070.014
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.266
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

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