In Search of Local Knowledge on ICTs in Africa
Bibliographic record
Abstract
This article explores whether, and to what extent, local knowledge features in research on the role of ICTs in statebuilding and peacebuilding in Africa, with a particular focus on neighboring Somalia, Kenya, and Ethiopia. We question whether the claims of the transformative power of ICTs are backed by ‘evidence’ and whether local knowledge – e.g., traditional mechanisms for conflict resolution – is taken into consideration by ICT-based development initiatives. To assess this, we systematically reviewed literature in the region, focusing on academic outputs as well as research published by non-governmental and governmental organizations. Several key findings emerged, including: 1) empirical evidence on the successful use of ICTs to promote peacebuilding and statebuilding is thin; 2) few differences exist between scholarship emanating from the Global North and from Africa; and 3) overall, the literature exhibits a simplistic assumption that ICTs will drive democratic development without sufficient consideration of how ICTs are actually used by the public.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".