Mobile Phones and Development: An Analysis of IDRC‐Supported Projects
Bibliographic record
Abstract
Abstract In the context of the rapid growth of mobile phone penetration in developing countries, mobile telephony is currently considered to be particularly important for development. Yet, until recently, very little systematic evidence was available that shed light on the developmental impacts of mobile telecommunication. The Information and Communication Technology for Development (ICT4D) program of the International Development Research Centre (IDRC), Canada, has played a critical role in filling some of the research gaps through its partnerships with several key actors in this area. The objective of this paper is to evaluate the case of mobile phones as a tool in solving development problems drawing from the evidence of IDRC supported projects. IDRC has supported around 20 projects that cut across several themes such as livelihoods, poverty reduction, health, education, the environment and disasters. The projects will be analyzed by theme in order to provide a thematic overview as well as a comparative analysis of the development role of mobile phones. In exploring the evidence from completed projects as well as the foci of new projects, the paper summarizes and critically assesses the key findings and suggests possible avenues for future research.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".