Processes of frugal social innovation: Creative approaches in underserved South African communities
Bibliographic record
Abstract
Abstract The article presents three case studies of frugal social innovation developed by groups of citizens from underserved communities of Cape Town, South Africa. The processes are analyzed to highlight how innovation emerged. Three factors were crucial: lack of resources, social transformation goals, and flexibility of the approach to technologies. Their combination allowed for creativity and inclusivity to become the drivers of the processes. The information and communication technology outcomes are innovative in the context and for the participants. More innovative are the processes, which maintained a high level of participation, a collective collaboration and a focus on the social transformative impact of the digital solutions. Furthermore, while much of the literature on frugal and social innovation has a business perspective whereby users are referred to as customers, the cases present community groups as innovators. This approach contributes to the development of a theory, which expands existing ones on frugal and social innovation. The principles derived from the analysis represent the contribution to practice in the ICT4D domain. They show how in a space with limited technical and procedural knowledge, it is possible to reduce the blinders toward innovation and operate in an ecosystem where participation, inclusion, and growth develop.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.035 | 0.028 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".