How are the Children of Visible Minority Immigrants Doing? An Update Based on the National Household Survey
Bibliographic record
Abstract
This paper examines the performance of the children of immigrants (called 2nd generation immigrants) to Canada using data from the 2011 National Household Survey, which was administered along with the 2011 Census. An encouraging fact revealed by the data is that 2nd generation visible minority immigrants are becoming more highly educated than both 2nd generation non-visible minority immigrants and non-immigrants: 53.4 per cent of 2nd generation visible minority between 25 and 44 with employment income had earned university certificates or degrees compared to only 35.4 per cent of non-visible minority 2nd generation immigrants and 25.2 per cent of non-immigrants in the same age groups. But, while 2nd generation visible minority immigrants obtained more education than 2nd generation non-visible minority immigrants and non-immigrants, their performance as a group did not measure up so well in the labour market. In the 25 to 44 age group 2nd generation visible minority immigrants earned on average $42,206, which was higher than the $40,431 earned by non-immigrants, but less the $49,202 earned by 2nd generation non-visible minority immigrants. The results from this study are broadly in line with its predecessor (Grady, 2011), but offer more encouragement for an improved performance of 2nd generation visible minority immigrants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".