Immersive Storytelling and an Afro- centric Future for XR
Bibliographic record
Abstract
This paper opens a conversation around a possible Afro-centric future for Immersive storytelling, particularly in XR, one that might challenge Western-centric approaches to XR (extended reality) technologies and storytelling methods. The author argues that African creators across the continent's 54 countries offer vital perspectives that could reshape global XR practices. Drawing on theoretical frameworks from postcolonial scholars like Trinh T. Minh-ha and Jaishree Odin, the paper positions spatial and immersive storytelling as an epistemological challenge to Western narrative traditions. It highlights successful African XR projects, including Joel Kachi Benson's award-winning VR work and initiatives from studios like Black Rhino and Electric South, while acknowledging persistent access barriers. The discussion explores the convergence of XR with Internet of Things (IoT) and Artificial Intelligence, proposing that diverse experimentation is crucial for the medium's maturation. The paper advocates for moving beyond mimetic approaches and mobile-centric development to embrace more varied storytelling traditions, particularly those grounded in African orality and participatory practices. This research suggests that an Afro-centric future for XR could significantly expand the medium's potential for global storytelling and cultural expression.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".