Burdens, Resilience, and Mutual Support: A Comparative Study of Families in China and South Korea Amid the COVID-19 Pandemic
Bibliographic record
Abstract
As the ongoing COVID-19 pandemic disrupts established daily routines, it underlines the role that families play in redefining social order and reorganizing people’s economic and social lives. In this paper, we investigate methods that Chinese and South Korean families have adopted to cope with the rapid changes brought on by the COVID-19 pandemic. We emphasize families’ responses to sudden changes in three domains: education and home schooling, economic downturn and unemployment, and mental health challenges. By integrating online education and remote working into a new form of family life and sharing economic and emotional risks among members, families shape social order during a period of unprecedented uncertainty. We highlight the fact that family members experience intensified work-family or school-family conflicts. Disadvantaged families are the most severely affected, as they are at high risk of job loss and lack proper protections. The absence of institutional protections and interventions has created further economic and emotional hardships for these vulnerable families. Informal and nonregular workers require more stable labor market conditions for the well-being of their families. Future research may include a systematic data collection and analysis of disadvantaged families, as this would offer a better understanding of the challenges and untenable choices that families face during times of crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".