Age-at-Arrival's Effects on Asian Immigrants’ Socioeconomic Outcomes in Canada and the U.S.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it