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Record W2490431169 · doi:10.1108/lodj-10-2014-0202

Transformational leadership in an extreme context

2016· article· en· W2490431169 on OpenAlexaffabout
Kara A. Arnold, Catherine Loughlin, Megan M. Walsh

Bibliographic record

VenueLeadership & Organization Development Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsSaint Mary's UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsTransformational leadershipContext (archaeology)Social psychologyOriginalityPsychologyLeadership styleSituational ethicsIdentity (music)Value (mathematics)Leadership studiesLeadership developmentLeadershipShared leadershipPublic relationsSocial identity theoryPolitical scienceSocial group

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to explore how male and female leaders define effective leadership in an extreme context. Design/methodology/approach – The authors conducted in-depth interviews with leaders working in an extreme context (a matched sample of female and male Majors and Colonels in the Canadian Armed Forces) and analysed military training materials. Findings – In the military, male and female leadership looks much more similar than might be expected. Further, surprisingly this is not occurring because women are leading in more masculine ways, but rather the opposite; men are leading in more feminine ways. Practical implications – There is a need for organizations to recognize and acknowledge the role of feminine leadership behaviours. This may also give women a better opportunity to succeed in these types of leadership roles. Originality/value – This study contributes to the leadership literature by furthering our understanding of the boundary conditions for transformational leadership in relation to gender stereotypes, situational strength, and social identity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.537
GPT teacher head0.298
Teacher spread0.239 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

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