Info Sheet 21: Asian-Canadian Youths’ Pandemic Experiences Through Visual Arts
Bibliographic record
Abstract
The COVID-19 pandemic lockdowns and the associated disruptions such as school closures, isolation, cancelled events, and missed milestones had an emotional toll on Canadian youth, making them highly vulnerable to the impacts of the pandemic (Ferguson et al., 2021). The pandemic intensified existing health and socioeconomic disparities that immigrants face in diverse settings (Khanlou et al., 2020), differentially and disproportionality impacting racialized communities (Gopal & Adesara, 2020). In the earlier stages those identifying as Asian-Canadian were especially affected (Cheng et al, 2021; Choi et al., 2021). Identity is a distinguishing character of an individual. A recent study found university students in Canada and in Spain were increasingly reporting higher rates of mental health problems relating to identity concerns (Gfellner et al., 2024). The 2023 Canadian Health Survey on Children and Youth found a decline in mental health and optimism about school from the pre-pandemic period amounts all young persons (Statistics Canada, 2024). Our ongoing study explores the impacts of the pandemic on the identities, sense of belonging, and agency of Asian-Canadian youth. In this Information Sheet we report on some of the educational and mental health challenges that youth experienced as a result of the 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.254 | 0.031 |
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".