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Record W2053805414 · doi:10.1002/jls.20218

Worldviews and leadership: Thinking and acting the bigger pictures

2011· article· en· W2053805414 on OpenAlexaff
John Valk, Stephan Belding, Alicia D. Crumpton, Nathan Harter, Jonathan Reams

Bibliographic record

VenueJournal of Leadership Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsVisionAction (physics)Face (sociological concept)Set (abstract data type)EpistemologyRelevance (law)SociologyPsychologyPublic relationsPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Leadership is about ideas and actions. Put simply, it is about implementing new ideas into creative actions to achieve desired results. Doing so, however, is far from simple. We know leadership requires considerable skills and abilities. It requires knowledge and insight—about one's organization or entity, its people, goals, strengths and market niche. Yet, something more is needed. Leadership also requires a kind of awareness beyond the immediate, an awareness of the larger pictures—of paradigms that direct us, beliefs that sustain us, values that guide us and principles that motivate us, our worldviews. This article will, first, briefly examine how the concept of worldviews is used in leadership study and the contexts in which it arises. Second, it will critically look at worldviews, recognizing that they are not always coherent and that our belief systems are often fragmented and incomplete. Third, it will argue for the relevance of the concept worldview in leadership study as a way to explore various visions of life and ways of life that may be helpful in overcoming the challenges we face today. Fourth, it will examine how national and global issues impact worldview construction, especially among the millennial generation. Our conclusions set some directions for leadership action in light of worldview issues.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.570
GPT teacher head0.388
Teacher spread0.183 · 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.

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

Citations10
Published2011
Admission routes1
Has abstractyes

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