Differential Impacts during COVID-19 in \nCanada: A Look at Diverse Individuals \nand Their Businesses
Bibliographic record
Abstract
La pandémie causée par le coronavirus 2019 (COVID-19) touche tous les pans de la société. Les auteurs s'intéressent aux répercussions économiques et sociales de la pandémie sur divers groupes au Canada, notamment ceux des femmes, des immigrants, des populations autochtones, des personnes handicapées et des groupes racialisés. À l'aide de deux vastes sondages en ligne réalisés par Statistique Canada, qui ne sont ni aléatoires ni pondérés pour représenter la population canadienne, ils analysent les écarts quantita tifs dans les défis et les préoccupations liés à la pandémie que mentionnent les femmes et les hommes, les immigrants et les Canadiens de souche, de même que les groupes intersectionnels, tant à titre personnel qu'en qualité de propriétaires ou de représentants d'entreprises. À l'intérieur des échantillons, constatent ils, les participants de certains groupes et leurs entreprises sont plus gravement affectés que d'autres par la COVID-19. Abstract: The coronavirus disease 2019 (COVID-19) pandemic is affecting all segments of society. This study in vestigates the pandemic's economic and social impacts on diverse groups in Canada, including women, immigrants, Indigenous peoples, persons with disabilities, and racialized people. Using two large online Statistics Canada surveys, which are neither random nor weighted to represent the Canadian population, we consider quantitative differences in the pandemic challenges and concerns reported by women and men, immigrants and those born in Canada, and intersectional groups, both as individuals and as the busi nesses they own or represent. Within the samples, individuals from diverse groups and their businesses are more negatively affected by COVID-19.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".