Are the Effects of COVID-19 on Inequality in Tertiary Education in Ghana Gendered?
Bibliographic record
Abstract
Educational institutions around the world were hit hard by the COVID-19 pandemic as there were nationwide closures of educational institutions around the world to contain the spread of the virus, resulting in the migration of teaching and learning to online platforms. This study examines the effects of the COVID-19 pandemic on inequality in tertiary education in Ghana, focusing on the gendered effects. Primary data were collected from 371 students from six selected public universities in Ghana mainly online using KoboCollect. Binary logistic regression was employed in the data analysis. The results show that the COVID-19-induced universities' closure and migration of teaching and learning to online platforms accentuated inequalities in learning opportunities by university students in Ghana, just that its effects are not gendered. Location significantly explained the observed inequalities experienced during the period of the universities’ closure and online teaching and learning. It is recommended that universities should embrace online systems as part of their teaching and learning practices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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 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".