Online Learning Experiences of Canadian Black Nova Scotians during Covid-19: Adopting an Intersectionality Framework
Bibliographic record
Abstract
Though school closures due to the COVID-19 pandemic affected all students globally, the effect was significantly more for students from marginalized and vulnerable communities. In Nova Scotia, Canada, the concern was the racial achievement gap that the education system is addressing through an inclusive education policy. The worry, especially for Black Nova Scotian students, was the online learning demands and the associated challenges. Through an analysis of a household survey and intersectionality framework, we explored these challenges. We argue that students have multiple and simultaneously acting identities that lead to differential learning experiences and outcomes, and an intersectionality approach should be considered to inform education improvement decisions. Keywords: online learning, Black Canadians, intersectionality, household survey, structural equation modelling Bien que les fermetures d'écoles dues à la pandémie de COVID-19 aient touché tous les élèves du monde, l'effet a été nettement plus marqué pour les élèves issus de communautés marginalisées et vulnérables. En Nouvelle-Écosse, au Canada, l'inquiétude portait sur l'écart de réussite raciale que le système d’éducation s'efforce de combler par une politique d'éducation inclusive. L'inquiétude, en particulier pour les élèves noirs de Nouvelle-Écosse, portait sur les exigences de l'apprentissage en ligne et les défis qui y sont associés. Par l'analyse d'une enquête auprès des ménages et d'un cadre d'intersectionnalité, nous avons exploré ces défis. Nous soutenons que les élèves ont des identités multiples qui agissent simultanément et mènent à des expériences et des résultats d'apprentissage différents, et qu'une approche d'intersectionnalité devrait être considérée pour informer les décisions portant sur l’amélioration de l'éducation. Mots clés : apprentissage en ligne, Canadiens noirs, intersectionnalité, enquête auprès des ménages, modélisation par équations structurelles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".