Engagement in the Knowledge Economy: Regional Patterns of Content Creation with a Focus on Sub-Saharan Africa
Bibliographic record
Abstract
The increasing digital connectivity has sparked many hopes about the democratization of information and knowledge production in Sub-Saharan Africa. To investigate the patterns of knowledge creation in the region and between other world regions we examine three key metrics: spatial distributions of academic articles (traditional knowledge production) and collaborative software development and Internet domain registrations (digitally-mediated knowledge production). We find that, contrary to the expectation of digital content to be more evenly geographically distributed than academic articles, the global and regional patterns of collaborative coding and domain registrations are more uneven than those of academic articles. Despite hopes of democratization afforded by the information revolution, Sub-Saharan Africa produces a lower share of digital content than academic articles. Our results suggest that the factors often framed as catalysts in the transformation to a knowledge economy do not relate to the three metrics uniformly. While connectivity is an important enabler of digital content creation, it seems to be only a necessary, not a sufficient condition: wealth, innovation capacity, and public spending on education are also important factors.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".