The impact of COVID-19 on the learning and achievement of vulnerable Canadian children and youth
Bibliographic record
Abstract
Many children and youth in Canada are identified as vulnerable due to educational, environmental, and social factors. They are more likely to be negatively affected by events that cause significant upheaval in daily life. The changes imposed by COVID-19, such as physical distancing, school closures, and reductions in community-based services all have the potential to weaken the systems of support necessary for these children to learn and develop. Existing inequities in educational outcomes experienced by vulnerable children prior to the pandemic have been greatly exacerbated as cracks in our support structures are revealed. Many children and youth have experienced disengagement, chronic attendance problems, declines in academic achievement, and decreased credit attainment during the pandemic, with the impact far deeper for those already at-risk. This chapter examines what is known to date regarding the impact of COVID-19 on vulnerable children and youth and provides recommendations to guide postpandemic planning. Vulnerable children, youth, and their families require access to reliable high-speed internet, effective and inclusive learning spaces, and a range of coordinated social services. All stakeholders need to develop and fund initiatives that address these critical areas to ensure that educational opportunities for all children and youth can be realized.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".