Some perspectives on understanding the adoption and implementation of ICT interventions in developing countries
Bibliographic record
Abstract
Research in the multi-disciplinary domain of ICT and Development indicates there is potential for information and communication technologies (ICT) to contribute to a nation’s socio-economic, socio-technical and socio-cultural development. With this in mind, developing countries have been rushing to implement ambitious ICT projects in rural areas through the direct/indirect supervision of institutions such as the World Bank, the United Nations (UN) and other donor/local agencies. These interventions aim to provide positive developmental impacts on people’s lives at an individual, group or community level. Interestingly, the main focus of the interventions has been on the implementation of ICT projects themselves, rather than on understanding their impacts at the recipient or community level; and such lack of understanding has led to many failures of ICT projects as reported. This paper highlights some important perspectives on research into ICT and Development while understanding the intentions behind the adoption and implementation of ICT interventions in developing countries. We propose a framework to encourage further investigation into ICT-led development projects which explicitly acknowledges the perspectives of: (i) the funding bodies, (ii) the organisations responsible for undertaking the intervention, and (iii) the to-be-affected community/ies, both dynamically and in context.
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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 | 0.073 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| 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".