Geographies of Information Inequality in Sub-Saharan Africa
Bibliographic record
Abstract
Introduction: Promises of Changing ConnectivitySub-Saharan Africa has traditionally been characterised by stark barriers to telecommunication and flows of information.Rates for long distance phone calls throughout Sub-Saharan Africa (SSA) used to be some of the highest in the world, and Internet costs and speeds similarly were out of the reach of all but the most privileged citizens.However, in the last few years, there have been radical changes to SSA's international connectivity.Fibre-optic cables have been laid throughout the continent and there are now over one hundred and fifty million Internet users and over seven hundred million mobile users in the region.This rapid transformation in the region's connectivity has encouraged politicians, journalists, academics, and citizens to speak of an ICT-fuelled revolution happening on the continent.Individuals and firms would increasingly be linked into global networks -interacting, selling and using knowledge through this connectivity (Graham & Mann 2013).This has also been reflected in new ambitions and policy in SSA.For example, in Rwanda (a strong advocate of upgrading connectivity to drive development) the stated policy goal has been to: "transform her subsistence agriculture dominated economy into a service-sector driven high value-added information and knowledge economy that can compete on the global market" (GoR 2001 p.7)Changing connectivity thus is articulated as a core driver of wider economic change in SSA.It is seen as providing a path for the region to move away from reliance on agriculture and extractive industries and towards a focus on the quaternary and quinary sectors (in other words, the knowledge-based parts of the economy).However, while much research has been conducted into the impacts of ICTs on older economic processes and practices, there remains surprisingly little research into the emergence of the new informationalised economy in Africa.As such, it is precisely now that we urgently need research to understand what impacts are observable, who benefits, who doesn't, and how these changes match up to our expectations for change.We need to ask if
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".