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Record W4283015757 · doi:10.3390/rel13060563

Recapturing the Oral Tradition of Storytelling in Spiritual Conversations with Older Adults: An Afro-Indigenous Approach

2022· article· en· W4283015757 on OpenAlexaff
Florence Juma

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsStorytellingIndigenousNarrativeContext (archaeology)Active listeningOral traditionRelevance (law)EthnographySociologyIntervention (counseling)Value (mathematics)PsychologyAestheticsAnthropologyHistoryPsychotherapistArtLiteratureComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The historical value of the oral tradition permeates literature as represented in multiple disciplines, including theology. An aspect of this tradition has proven viable in spiritual conversations with older adults. This paper will discuss the oral tradition’s medium of storytelling and listening to demonstrate its relevance in therapeutic conversations with older adults. Therapeutic storytelling is a distinct intervention prevalent in the African oral tradition This approach is also gaining attention in the contemporary context, blending seamlessly within the narrative approach. Using the quantitative research method of ethnography and autoethnography, I analyze specific therapeutic encounters that employ a storytelling/listening approach in spiritual conversations. The analysis reveals the relevance of storytelling in specific therapeutic encounters. Storytelling is gaining interest and reclaiming space in therapeutic settings with diverse populations, but mostly with older adults. The study also highlights the apparent similarities between the traditional approach to storytelling and the narrative approach in the contemporary therapeutic milieu.

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)0.008
metaresearch head score (Gemma)0.008
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 score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0100.015
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.258
Teacher spread0.223 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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