Immigrant gaps in parental time investments into children's human capital activities
Bibliographic record
Abstract
Current and future well-being and economic prosperity of children depend in large part on the nuances of decisions made by parents with respect to familial resources, an important part of which regard the time spent in the company of children. We estimate differences in the time that immigrant and Canadian-born parents allocate to child-care activities relative to other activities using the time diaries from the General Social Survey. We find that mothers born abroad spend more time at work and less time in leisure but there is no significant difference in time devoted to household production or child service between them and Canadian-born mothers. Despite not finding differences by immigration status in the total care-time parents provide for their children, we do find significant differences - by immigrant status - in time specifically devoted to human capital investment activities with children: African, Asian, European and South-Central American mothers spend up to 30 more minutes daily in these activities than the Canadian born. We further assess the patterns of time use of second-generation young adults and find that they spend more time on education and homework compared to third generation or higher young adults. This supports a plausible effect of the time invested in children's human capital generating activities by immigrant parents on their Canadian-born children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".