Development Informatics Research and the Challenges in Representing the Voice of Developing Country Researchers: A South African View
Bibliographic record
Abstract
Indigenous or local researchers from developing countries have not made a leading contribution to development informatics (DI) or information and communication technologies for development (ICT4D) research. This is noteworthy since these researchers should be in a prominent position to contribute to the discourse, where context knowledge is regarded as vital. Furthermore, a dependence on foreign scholarly direction can create a gap between research and reality in a way that affects the success of ICT programmes in African countries. Extant literature highlights this problem, but most studies stop short of considering the causes and proposing how to amplify the voice of developing country researchers. This paper documents the ICT4D/DI research discourse that took place during four seminal academic events in South Africa during the period 2012 to 2015. Those discussions are presented and analysed here to contribute to the wider discourse on ICT research and practice in developing countries, with the aim of enhancing the research contribution of developing countries. An interpretivist, involved researcher analysis of the workshop reports is conducted to gain an improved understanding of the South African ICT4D/DI researcher's challenges to proportional participation. While this study takes a South African perspective, many of the findings could apply to researchers in other developing countries.
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".