Digital Literacy Programmes in Nairobi Slums: Adoption Rates and Educational Outcomes Over Three Years
Bibliographic record
Abstract
Digital literacy programmes have become increasingly important in addressing educational disparities among youth living in Nairobi slums. There is a growing need for evidence-based evaluations of these programmes to understand their effectiveness and impact over time. The review utilised comprehensive searches across academic databases, including PubMed, Web of Science, and Google Scholar. Inclusion criteria were defined based on study design, participant age range (15-24 years), setting (Nairobi slums), and timeframe (three-year period). Studies were assessed for methodological quality using the Cochrane Risk of Bias tool. Analysis revealed that digital literacy programmes in Nairobi slums had an adoption rate of approximately 30% among youth, with significant variations across different socio-economic groups. Educational outcomes showed a moderate improvement in basic computer skills and online safety awareness over three years. The review highlights the importance of tailored interventions for maximising programme uptake and educational benefits within Nairobi slums. Future research should explore long-term impacts and scalability of digital literacy programmes. Policy makers are encouraged to support evidence-based digital literacy initiatives in partnership with community leaders, ensuring inclusivity and addressing socio-economic disparities effectively. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".