New Home, New Learning: Chinese Immigrants and Unpaid Housework and Care Work
Bibliographic record
Abstract
Abstract: This paper examines the learning experience of Chinese immigrants through unpaid housework and care work. Based on the interviews with 8 Chinese immigrants (4 women and 4 men) in the Great Toronto Area, who immigrated to Canada from Mainland China in the previous five years, this paper explores the challenges and difficulties these people encountered in their unpaid labour in the homeplace. In so doing, the paper tends to reveal the voluntary and involuntary housework or care work those immigrants engaged in, the visible and invisible learnings associated with this life transition and the diverse ways they acquired those learnings in their new home in Canada. It is 9:00pm. I put on the table, also used as my reading desk, the dishes I cooked for dinner: white rice, soup, green vegetables, roast chicken and dessert, a combination of Chinese and Western food. During dinner, I shared with my daughter, a Grade 11 student, a bad experience I had that day. I learned that afternoon at our regular project meeting that I didn’t get a summer graduate assistantship because of my misunderstanding of the procedure for applying for a summer GA. During the meeting I unintentionally turned away for something else and didn’t realize until after the meeting that I was actually expected to join the discussion on something I
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".