Emerging Gender, Media and Technology Scholarship in Africa: Opportunities and Conundrums in African Women’s Navigating Digital Media
Bibliographic record
Abstract
Over the past decade and earlier, much of the academic and grey literature has painted an optimistic picture of rapidly increasing access and growth of digital technologies in Africa. Industry statistics put internet penetration in Africa close to 40 percent and growing, even though the continent still lags behind the world average of Internet users (Internet World Statistics, June 2019). Some estimates predict that by 2025 the sub-continent will add 167 million mobile subscribers to its existing 456 million (GSMA Report, 2019). Mobile devices, especially, have assumed centrality in the lives of ordinary people and provide prospects for Africa to leapfrog into the modern digital world. Smart phones are enabling millions of Africans to share news and information more easily and to tap into all kinds of essential services, much like elsewhere in the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".