Participatory Arts-based Game Design: Mela, a Serious Game to Address SGBV in Ethiopia
Bibliographic record
Abstract
The emerging body of work on participatory game design (PGD) highlights the significance of working with end-users’ voices as the starting point. This is particularly critical in serious games that seek to impact social change in areas such as sexual and gender-based violence (SGBV). This article, which is based on fieldwork with 16 college instructors in four agricultural colleges in rural Ethiopia, draws together concepts of participatory visual methods (particularly cellphilming), PGD and a game universe perspective to offer an engaging and interactive approach to the design of serious games. We refer to this as ‘Participatory Arts-based Game Design’ (PAGD), an approach that was used to create Mela, a serious game to address SGBV in Ethiopian agriculture colleges. Exploring Mela game’s participatory and engaging design process, this article offers a framework for serious game development to address critical social change issues that go beyond the game itself. It has the potential to not only place the end-users at the centre but to recognize the critical role of engagement and immersivity in a field oriented towards impact and sustainability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".