COVID-19 Lockdown and higher education. Time to look at disasterpreparedness as a governance issue?
Bibliographic record
Abstract
When COVID-19 broke out in Wuhan in 2019, the world looked at it as a ‘Chinese’ disease and no international efforts were made to assist China. Countries that supported China just offered solidarity messages. China adopted a raft of measures that included prescribing a lockdown on Wuhan and construction of infrastructure such as hospitals. In 2020, COVID-19 extended its tentacles across the globe. A majority of countries adopted the lockdown as a mitigatory measure. The lockdown activated an extraordinary instant emergency in the education sector as schools, colleges and universities shut down. What worsened the situation was that no solution was in sight as the medical researchers dithered from one suggestion to the other. This paper examines possible ways to deal with the emergency in the education sector by suggesting alternative learning solutions. The major argument of this paper is that countries should not simply copy and paste solutions that are not in sync with their local settings. Using the multistage designs sampling technique, three universities from a target of eighteen were selected. Convenience sampling was used to select the three universities and analytic rubrics were used to analyse clarity of policy and disaster preparedness by universities in Zimbabwe. For comparative purposes, four international webinars on education and COVID-19 were selected. This paper contributes towards addressing the lacunae created by global lockdown and subsequent shutdown of learning institutions due to COVID-19. The findings were that the lockdown approach was adopted and implemented without adaptation. Learning institutions were closed indefinitely despite the economic environment, the digital divide and the rural-urban divide militating against lockdown’s entire adoption. Key proposals to deal with the lockdown include scaling up distance education based on mixed technologies, a paradigm shift on perceptions on digital education and resuscitation of postal services.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".