How COVID-19 Has Exacerbated Inequality in Higher Education in Brazil
Bibliographic record
Abstract
Drawing on educational census data and a review of news articles and higher education policies in Brazil, this article examines the impact of COVID-19 on the access and retention of the low-income Brazilian population in higher education. Guided by the question, “What is the impact of COVID-19 on the most vulnerable population in Brazil in terms of access to, and retention in higher education?”, the paper is structured in two sections: the first offers a short historical overview of Brazilian higher education; the second examines the impact of the pandemic on student retention in higher education, looking at factors such as social isolation, job and income precarity, use of Information and Communication Technologies (ICT), internet access, and technological resources. I argue that distance education offered by private higher education institutions benefits the privileged students and that the effects of the pandemic are detrimental to the socially disadvantaged students since those who are in public universities do not always have access to technology, and those who study in private universities feel the impact of not being able to pay tuition fees, besides the loss of several jobs in different sectors. In conclusion, I recommend policy initiatives to improve access to higher education.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".