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Record W2588998240 · doi:10.1002/joe.21777

Spiritual Intelligence: Going Beyond IQ and EQ to Develop Resilient Leaders

2017· article· en· W2588998240 on OpenAlexaboutno aff
Stephen K. Hacker, Marvin Washington

Bibliographic record

VenueGlobal Business and Organizational Excellence · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsEmotional intelligenceCompetence (human resources)PsychologyLeadership developmentCurriculumSpiritual intelligenceApplied psychologyMedical educationManagementPedagogyPublic relationsSocial psychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

University‐based leadership development programs have long included a focus on human intelligence and emotional intelligence to foster subject and relationship competence, respectively. In response to the need to develop leaders with a strong sense of individual purpose, vision, and values who can meet the challenges of a volatile business environment, spiritual intelligence, a measure of inner resilience, is being added to executive training curricula. All three elements were addressed in a four‐year leadership development program conducted through the University of Alberta for the Alberta Heath Services and the Royal Canadian Mounted Police. The results of post‐program self‐assessments and 360‐degree evaluations of the 160 program participants highlight the value of this three‐tier training method in molding a new generation of resilient leaders. © 2017 Wiley Periodicals, Inc.

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)0.003
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.048
GPT teacher head0.322
Teacher spread0.273 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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