Implications of COVID-19 to Young Women’s Online Education in Canada
Bibliographic record
Abstract
This paper will explore the gendered implications of online education during the pandemic in Canada.The COVID-19 pandemic shifted the educational environment by transporting in-person social developments within conventional school structures into an online and unfamiliar environment ( Bilodeau, Kehler, and Minnema 2021 ).In this new setting, young women face a range of barriers to education and exacerbated forms of cyberbullying ( Kusumawaty et al. 2021 ; Mkhize and Goapl 2021 ).Moving forward with the Women, Peace, and Security (WPS) agenda and efforts for recovery (as mentioned by K.C. and Mackenzie in the forum introduction), Canada's COVID-19 educational recovery plan requires a comprehensive gendered perspective, with attention to the impacts of COVID-19 on young women's education.This paper explores the intersectional gendered impacts of educational shifts associated with COVID-19, with attention to unpaid labor, unequal access to technology, unreliable access to health resources, and cyberbullying.Although these were preexisting issues for young women, this paper highlights how the pandemic exacerbated them in ways that could have long-term implications for education quality and equity.Conducting schooling online and within the home has contributed to the retraditionalization of gender roles.Young women students are more likely to be relegated to unpaid and additional home-making labor during periods of school closures ( Reliefweb 2021 ; Canadian Partnership for Women and Children's Health 2022 ).This impedes their ability to balance educational responsibilities with homemaking duties.Elevated rates of stress and decreasing resilience to educational changes can lead to education disengagement ( Whitley, Beauchamp, and Brown 2021 ), resulting in poorer results in reading, math, and sciences among youth ( Frenette, Frank, and Deng 2020 ).These factors are compounded by attitudinal factors toward the expectation of women to maintain the home while minimizing the need for digital skills ( Webb et al. 2021 ).This argument corroborates Azmi's findings (from this forum) on women migrant returnees who face diverse economic and domestic challenges upon their return home during the pandemic, intensified by existing inequalities.Research indicates that a decrease in access to quality education over time can lead to sustained disadvantages in future vocational opportunities ( Maldonado and De Witte 2022 ;Sabates, Carter, and Stern 2023 ).While this analysis warns of the impact from gender roles and domestic challenges to education,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".