The Necessity of Community Collaborations in Supporting Newcomer Student Learning: Lessons Learned from the <scp>covid</scp> -19 Pandemic
Bibliographic record
Abstract
This article captures the results of a study illustrating the challenges experienced by newcomer youth to Canada in adapting to online learning between March and June 2020. A collaborative research team consisting of a local immigrant-serving agency, local school board and educators, and a group of interdisciplinary university researchers conducted a qualitative study to explore educational challenges from the perspectives of high school-aged youth and parents of elementary school students. We found that the cov id -19 crisis exposed the fissures in the education system whereby those most in need of the supposed support offered by the education system were not intentionally included in organizational policies and procedures, thus further exacerbating educational inequities and compounding the pre-Covid challenges students experienced. This study also models collaborative and community-centred research on how educators and school boards could work with community supporting agencies to provide support for newcomer youth during and beyond the Covid-19 pandemic.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".