The Use of Theater to Restore Intergenerational Relationships and Knowledge Transfer in Africa: A Narrative Review
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".