COVID-19 and Education Disruption in Ontario: Emerging Evidence on Impacts
Bibliographic record
Abstract
The COVID-19 pandemic has led to significant education disruption in Ontario. This has included mass and localized school closures, multiple models of educational provision and gaps in support for students with disabilities. The unequal distribution of school closures and pandemic-associated hardships, particularly affecting low-income families in which racialized and Indigenous groups, newcomers and people with disabilities are overrepresented, appear to be deepening and accelerating inequities in education outcomes, wherever data have been collected. Further, there are health risks associated with closures including significant physical, mental health and safety harms for students and children. Modelling suggests long-term impacts on students’ lifetime earnings and the national economy. There are substantial data gaps on the impact of closures on Ontario’s children. However, existing information and analysis can inform strategies to minimize further pandemic disruptions to children’s education and development. Identifying or tracking areas where students are facing the greatest challenges in the wake of COVID-19 and implementing systematic supports to address pandemic-associated educational harms are critical to minimizing the overall impact and supporting recovery.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".