Age-at-Arrival's Effects on Asian Immigrants’ Socioeconomic Outcomes in Canada and the U.S.
Bibliographic record
Abstract
Age-at-arrival is a key predictor of many immigrant outcomes, but discussion continues over how to best measure and study its effects. This research replicates and extends a pioneering study by Myers, Gao, and Emeka [International Migration Review (2009) 43:205–229] on age-at-arrival effects among Mexican immigrants in the U.S. to see if similar results hold for other immigrant groups and in other countries. We examine data from the 2000 U.S. census and 2006 American Community Survey, and 1991, 2001, and 2006 Canadian censuses to assess several measures of age-at-arrival effects on Asian immigrants’ socioeconomic outcomes. We confirm several of Myers et al.'s key findings, including the absence of clear breakpoints in age-at-arrival effects for all outcomes and the superiority of continuous measures of age-at-arrival. Additional analysis reveals different age-at-arrival effects by gender and Asian ethnicity. We suggest guidelines, supplementing those offered by Myers et al., for measuring and studying age-at-arrival's effects on immigrant outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".