A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis
Bibliographic record
Abstract
Uncertain times require prompt reflexes to survive and this study is a collaborative reflex to better understand uncertainty and navigate through it. The Coronavirus (Covid-19) pandemic hit hard and interrupted many dimensions of our lives, particularly education. As a response to interruption of education due to the Covid-19 pandemic, this study is a collaborative reaction that narrates the overall view, reflections from the K12 and higher educational landscape, lessons learned and suggestions from a total of 31 countries across the world with a representation of 62.7% of the whole world population. In addition to the value of each case by country, the synthesis of this research suggests that the current practices can be defined as emergency remote education and this practice is different from planned practices such as distance education, online learning or other derivations. Above all, this study points out how social injustice, inequity and the digital divide have been exacerbated during the pandemic and need unique and targeted measures if they are to be addressed. While there are support communities and mechanisms, parents are overburdened between regular daily/professional duties and emerging educational roles, and all parties are experiencing trauma, psychological pressure and anxiety to various degrees, which necessitates a pedagogy of care, affection and empathy. In terms of educational processes, the interruption of education signifies the importance of openness in education and highlights issues that should be taken into consideration such as using alternative assessment and evaluation methods as well as concerns about surveillance, ethics, and data privacy resulting from nearly exclusive dependency on online solutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".