The Well-Being of Immigrant Children and Parents in Canada
Bibliographic record
Abstract
In this paper, we use microdata from the Canada Community Health Survey (CCHS) to document the fact that both immigrant children and immigrant parents have lower self-reported life satisfaction and are less likely to feel a strong sense of „belonging‟ to their local communities than their Canadian-born peers. A novel aspect of our work is that we provide direct comparisons of both levels and correlates of well-being for parents and children, since our data asks children (aged 12 to 17) and adults the same survey questions. We find, first, that immigrant status has a larger, negative, association with well-being for parents than for children. And, although income is an important correlate of life satisfaction for both parents and children, the association is larger for parents. A troubling finding is that there is no apparent improvement in life satisfaction for immigrant parents or children who have lived longer in Canada. Given European experiences with alienation among immigrant youth, we also examine „belonging to the community‟ as another aspect of well-being; lower levels of belonging are reported by immigrant youth, especially girls, than by their Canadian peers. Indeed, for girls, immigrant status is one of the largest (negative) correlates of belonging identified. The same is true for parents, but the size of the association is smaller and appears to decline over time.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".