Impact of Covid-19 on Teaching and Learning in Africa Assessed by the Education Unions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study investigated the situation and views of the Education International (EI) member unions in Africa regarding the Covid-19 pandemic. The EI, a global body of education unions with over 32.5 million members in 384 unions across 178 countries in the world, is a critical global education stakeholder. It commissioned this study to obtain evidence to inform its policies about the pandemic. The primary data are based on the opinions of union leaders from 58 education unions in 34 African countries who responded to a semi-structured online questionnaire, while additionally, thirteen union leaders across the African countries and the Chief Regional Coordinator of the EI Africa Region were interviewed. The findings revealed a massive disruption of education, exacerbated educational inequalities, teachers’ poor digital skills and lack of infrastructure, and increased vulnerability of the marginalized learners shut out of school. Recommendations were made for EI, African Union and Governments.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it