The Impact of Open Development Initiatives in Lower‐ and Middle Income Countries: A Review of the Literature
Bibliographic record
Abstract
Abstract The purpose of this paper is to explore the field of open development in lower and middle income countries (LMIC) through a review of the literature. We examined 269 articles between 2010 and 2015, that were retrieved through keyword searches of the Scopus database and four ICT4D journals. This article adopts the pathway of effects model to analyze contributions according to inputs, mechanisms and outputs of open initiatives in LMICs. The review finds a fairly even spread of articles across the three stages of effects. Studies that disentangled reasons why or why not openness makes a difference provided the most insight to underlying mechanisms and impact of open initiatives. We found very little evidence that research within this area is concerned with the perspectives of poor and marginalized people – notably women. We therefore question the normative value of open development as a means to transform power relations. However, we argue that a more concentrated vision within this field is needed to exploit the full potential of digitally enabled openness for development.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".