Developing Persuasive Mobile Games for African Rural Audiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advent of cheap Android phones in the African Tech market, most people living in the rural areas of many African countries now have access to smartphones. These phones give them the opportunity to have an almost identical mobile phone experience as people living in urban areas or the Western world. This development opens a window of opportunity to leverage this high penetration of mobile devices to design application such as persuasive game interventions to assist individuals living in these communities to modify, change or shape their behaviours and attitudes in a desirable way. This paper explores the challenges and issues encountered in the design and use of persuasive mobile games as a tool to promote behaviour change among people living in the Rural African communities. It also highlights how these challenges affect the implementation of persuasive strategies, suggests design solutions for overcoming these challenges, and how persuasive games can be optimized to be appropriate for the target rural African populations. Some of these challenges are technically oriented (internet connectivity issues) while others are non-technically oriented (language diversity).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it