Grassroots Arts Management Models in Children's Theatre: A Case Study of Footprints of David Art Foundation, Bariga
Bibliographic record
Abstract
This article investigates the management models sustaining self-organised grassroots children’s theatre in Nigeria’s Creative and cultural sector, focusing on the Footprints of David Art Foundation (FODAF) in Bariga, Lagos state. While grassroots arts organisations play a significant role in nurturing creative skills in underserved communities, the specific organisational structures and management strategies that enable their success remain under-researched. Using a qualitative case study approach, this study interrogates the operational framework of FODAF to propose the R.O.O.T.S (Resourceful, Organic, Open/Collective, Tradition-Grounded, Spirit-Driven) Model as a new conceptual tool for analysing grassroots arts management. Theoretically, the paper integrates Cultural Democracy Theory, Social Capital Theory and Indigenous Performance Theory to frame the analysis. The findings posit that the R.O.O.T.S. model effectively explains how initiatives like FODAF emerge as counterspaces to inadequate cultural infrastructure, leveraging inclusive, ‘careful’ work models and social networks to achieve sustainability and community impact. This research contributes a novel framework to academic discourse on arts management, child arts and the community-based creative economy in Africa, offering a transferable model for understanding similar cultural organizations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".