The Evolution of the Internet in Ethiopia and Rwanda: Towards a “Developmental” Model?
Bibliographic record
Abstract
The Internet in Africa has become an increasingly contested space, where competing ideas of development and society battle for hegemony. By comparing the evolution of the Internet in Ethiopia and Rwanda, we question whether policies and projects emerging from two of Africa’s fastest growing, but also most tightly controlled countries, can be understood as part of a relatively cohesive model of the ‘developmental’ Internet, which challenges mainstream conceptions. Our answer is a qualified yes. Ethiopia and Rwanda have shared an overarching strategy which places the state as the prime mover in the development of Internet policy and large-scale ICT projects. Rwanda, however, appears to have developed a more open model which can accommodate a greater variety of actors and opinions, and incorporate them within a relatively coherent vision that emanates from the centre. Ethiopia, in contrast, has developed a more closed model, where all powers rest firmly in the hands of a government that has refused (so far) to entertain and engage with alternative ideas of the Internet. In the case of Rwanda, we argue, this approach reflects broader strategies adopted by the government in the economic domain but appears to counter the prevailing political approach of the government, allowing for a greater degree of freedom on the Internet as compared to traditional media. While in the case of Ethiopia, the opposite is true; Ethiopia’s Internet policies appear to run counter to prevailing economic policies but fit tightly with the government’s approach to politics and governance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".