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Record W4214681448 · doi:10.21810/jicw.v4i3.4196

Effective Leadership in Times of Uncertainty

2022· article· en· W4214681448 on OpenAlexvenueaboutno aff
Vincent Virk

Bibliographic record

VenueThe Journal of Intelligence Conflict and Warfare · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsnot available
Fundersnot available
KeywordsOfficerPresentation (obstetrics)Period (music)Political scienceLeadership developmentKey (lock)Public relationsManagementPsychologyLawMedicineComputer scienceComputer securityArtEconomics

Abstract

fetched live from OpenAlex

On November 26, 2021, Lt. Col. Vincent Virk, Commanding Officer of the British Columbia Regiment (Duke of Connaught’s Own), presented Effective Leadership in Times of Uncertainty at the 2021 CASIS West Coast Security Conference. The presentation was followed by a question and answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were the three spheres of leadership (institutional, people, self), genuine leadership requiring development, and the importance of individuals and the team.

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)0.012
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.009
Scholarly communication0.0120.008
Open science0.0010.012
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0110.002

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 head0.176
GPT teacher head0.366
Teacher spread0.190 · 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 designTheoretical or conceptual
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
Published2022
Admission routes2
Has abstractyes

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