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Record W2749564785

Who am I? : bi-vocational ministers and pastoral identity

2017· article· en· W2749564785 on OpenAlexaboutno aff
Kate Jones

Bibliographic record

VenueSaint Mary's University Institutional Repository (Saint Mary's University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTheological Perspectives and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Vocational educationSociologyPolitical sciencePedagogyArtAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Who Am I? Bi-Vocational Ministers and Pastoral Identity"Author: Kate Jones "Who Am I? Bi-Vocational Ministers and Pastoral Identity" is a Hermeneutic Phenomenological study that examines the experience of bi-vocational ministers in the United Church of Canada as they navigate their vocational identity.Bi-vocational ministers who are navigating two vocations simultaneously were interviewed and the data obtained from these interviews was analyzed.Several themes emerged from the interviews: the bi-vocational ministers felt strongly called to be in bi-vocational ministry; they experienced the different threads of their identity, vocational and otherwise, as multiplicity; their experience of navigating this multiplicity can be understood through a framework that mirrors the intra-trinitarian relationship; bi-vocational ministers experience an urgency to develop good boundaries; and the bi-vocational ministers interviewed had developed a variety of concrete models or images through which they interpret their identity.There are many implications of this research for the church with an anticipated increase in the number of bi-vocational ministers.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0280.020
Scholarly communication0.0070.004
Open science0.0010.007
Research integrity0.0020.005
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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