Revisiting global development frameworks and research on universal basic education in Ghana and Sub‐Saharan Africa: a review of evidence and gaps for future research
Bibliographic record
Abstract
The emergence of global development frameworks such as Education for All, Millennium Development Goals, and Sustainable Development Goals have expanded opportunities for Universal Basic Education (UBE) in Ghana and Sub‐Saharan Africa (SSA). In the three decades of their implementation, these frameworks have also stimulated a culture of research based on measuring development and educational outcomes through established indicator‐based approaches. Subsequently, research on UBE in Ghana and SSA remains largely dominated by quantitative indicators which concentrate on enrolment and completion numbers in measuring a country’s progress. Yet, emerging literature shows that the expansion in enrolment is accompanied by high rates of drop‐outs, non completion, and low learning outcomes even for those able to complete basic education. Using structured and unstructured procedures to identify both academic and grey literature, this review explores the state of educational expansion and research on UBE in Ghana and SSA. We argue that the current reliance on dominant quantitative, indicator‐based approaches to assessing UBE reveals little about the differential experiences of children, particularly those in rural and marginalised communities, who receive poor quality education. The lack of information about children’s experiences of access reinforces inequalities in education, employment, and upward mobility in later life. Future research should seek to unpack micro‐level experiences which characterise access, as well as the pathways through which factors such as poverty and location create unequal experiences in schooling access, to inform context‐specific policies for UBE.
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 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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".