Agents of Technology Localization in East Africa: Case Studies of Social Enterprises in Tanzania
Bibliographic record
Abstract
Technology localization refers to activities that seek to make particular technologies locally functional and locally embedded in order to overcome resistance to their adoption. These activities can be described as diffusion, institutional support, and technical adaptation. In developing societies that face experiences of resistance to technological change, several organizational agents could serve as agents of localization. This paper showcases a number of social enterprises in East Africa – particularly in Tanzania – that are involved in localizing technologies for sustainable energy and agricultural mechanization. Field data were collected between December 2014 and September 2015. Staff, clients and partners of the social enterprises were interviewed. In addition, field observations and a scan of accessible reports and documents of social enterprises and their partner organizations took place. The cases demonstrate technology localization activities and assess the effectiveness of these social enterprises as agents of localization. The study concluded that, given appropriate tools and context, such as engaging early adopters of innovation and staying attuned to feedback from local communities, social enterprises can be effective agents of technology localization.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".