Technological Change and Techno-Social Systems: Re-Examining Sustainable Development and Digitalisation in Africa
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
This article argues that understanding currently dominant technological change models in low-and-middle income countries is important to addressing challenges to sustainable development with enhanced knowledge and effective policies. Combining such understanding with using systems thinking, as a theoretical framework, helps in illuminating techno-social systems and their overlaps with economic and human development systems, therefore highlighting possible leverage points for interventions to usher technological change towards sustainable development objectives. The proposed conceptual synthesis between technological change models and systems thinking is then critically applied to case studies related to digitalisation in Africa, where challenges to sustainability are amplified by continuous pressure for technological advancement, making local capabilities a central issue. The case studies examine how continental digitalisation indicators are ahead of industrialisation and human development indicators, with similar issues in digitalising agri-food ecosystems. We show that, while Africa is currently increasing in digitalisation, correlations between digitalisation and sustainable development are not as direct, or necessarily positive, as initially assumed. Similar trends are seen in digitalisation and agri-food ecosystems, where farm-raised data is monetised off-farm, thus removing opportunities for farmers to realise return on their knowledge investment. Examining the cases, using the proposed synthesis approach, reveals that digitalisation can contribute to development indicators when coupled with enhancing employment in productive sectors and that the prevailing order of ‘technology-push, demand-pull’ models suggests more investment in technological improvement. The article contributes to theory by illuminating overlaps between two theoretical/conceptual areas and to praxis by informing alternative policy directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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