New Home, New Learning: Chinese Immigrants, Unpaid Household Work, and Lifelong Learning
Bibliographic record
Abstract
Literature on lifelong learning indicates that major life transitions lead to significant learning. However, compared to learning in paid jobs, learning in and through household work has received little attention, given the unpaid nature and the private sphere where the learning occurs. The current study examined the changes and the learning involved in three aspects of household work: food work, childcare/parenting, and emotion work among recent Chinese immigrants in Canada. This study draws on data from a Canadian Survey on Work and Lifelong Learning (WALL), 20 individual interviews, a focus group, and a discussion group with new Chinese professional immigrants in the Greater Toronto Area. The results indicate that food work and childcare increased dramatically after immigration due to a sudden decline of economic resources and the lack of social support network for childcare. Emotion work intensified due to the challenges in paid jobs and the absence of extended families in the new homeland. To adapt to the changes in their social and economic situations, and to integrate into the Canadian society, Chinese immigrants learned new beliefs and practices about food and childrearing, developed new knowledge and skills in cooking and grocery shopping, in childcare and disciplining, in solving conflicts with children and spouses, and in transnational kin maintenance. In addition, the Chinese immigrants also developed new views about family, paid and unpaid work, meaning of life, and new gender and ethnic identities. However, these dramatic changes did not shatter the gendered division of household work. Both the qualitative and the quantitative data suggest that women not only do more but also different types of household tasks. As a result, it is not surprising that both the content and the ways of learning associated with household work varied by gender, class, and ethnicity. By exploring learning involved in the four dimensions of household work: physical, mental, emotional, and spiritual, this dissertation demonstrates that learning is both lifelong and lifewide. By making household work visible, this research helps make visible the value of the unpaid work and the learning involved in it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".