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Record W7125715715 · doi:10.17169/fqs-27.1.4440

The Use of Theater to Restore Intergenerational Relationships and Knowledge Transfer in Africa: A Narrative Review

2025· article· en· W7125715715 on OpenAlexaff
Stéphanie Caron-Roy, Bonnie Fournier, Roula Keitly-Hawa, Susan Sommerfeldt, Artem Mamadzhanov

Bibliographic record

VenueForum: Qualitative Social Research (Freie Universität Berlin) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of AlbertaWestern UniversityThompson Rivers University
Fundersnot available
KeywordsNarrativeAction (physics)Citizen journalismKnowledge transferParticipatory action researchTraditional knowledgeSustainability

Abstract

fetched live from OpenAlex

Intergenerational knowledge transfer (IGKT) remains a significant challenge for northern Ugandan communities following 25-years of civil war, displacement, and continued strife in the region. In this narrative review, we explore what is known in relation to enhancing IGKT and the re-establishment of local traditions through the use of arts-based research methods, and specifically, applied theater. Using an integrative review search strategy and a qualitative assessment tool, we analyze relevant literature. Findings include six key areas where theater methods may be relevant in exploring IGKT in Africa. These are: Supporting childhood and human development; addressing gender inequities and generational structures; supporting conflict resolutions across generations; promoting HIV/AIDS prevention, awareness, and education; navigating ethical lessons and theater in Africa; fostering understanding and action on environmental sustainability and climate change. Researchers using participatory theater methods may offer insight into intergenerational relationships and knowledge transfer in the northern Ugandan post-conflict context, yet it is not without known ethical and material realities of such work in Africa.

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)0.022
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.002
Scholarly communication0.0000.001
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 head0.858
GPT teacher head0.686
Teacher spread0.172 · 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.

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
Published2025
Admission routes1
Has abstractyes

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