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Record W2221024645

An Analysis of Education and Experience in Pastoral Leadership Development

2015· article· en· W2221024645 on OpenAlexaboutno aff
Justin Hayes

Bibliographic record

VenueDigital Commons - Olivet Distinctive Heritage, Scholarship, & Creativity (Olivet Nazarene University) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsLeadership developmentPedagogySociologyPsychologyPublic relationsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This study analyzed the relationships between education and servant leadership behaviors and between experience and servant leadership behaviors. The goal was to determine whether education or experience served as the stronger predictor of servant leadership behaviors among Nazarene pastors. Several linear regressions were calculated using data submitted by 37 Nazarene pastors serving in the United States and Canada from a demographic survey designed by the researcher and the Servant Leadership Questionnaire. This study showed participating Nazarene pastors rate strongly in servant leadership behaviors, but no predictive relationships between education and servant leadership, between total years (full time and part time) of ministry experience and servant leadership, and between full time ministry experience and servant leadership were found. Similarly, additional multiple regressions showed no combination of these factors predicted servant leadership behaviors among Nazarene pastors. The researcher concluded Nazarene pastors serving in the United States and Canada likely possess strong ratings in servant leadership behaviors, but education and experience were not strong predictors of those behaviors.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.135
GPT teacher head0.360
Teacher spread0.225 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

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