Decreasing the Recent Immigrant Earnings Gap: The Impact of Canadian Credential Attainment
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
Abstract It is well documented that newly arrived immigrants face a significant earnings gap relative to native‐born workers. One way for new immigrants to improve their relative labour market position upon arrival in a host country is to improve their educational credentials. According to signalling theory, a host‐country credential should provide employers with a proxy for true productivity on the job, leading to higher earnings. Using data from a Canadian longitudinal survey, we employ longitudinal growth‐curve techniques to estimate the effect of receiving a Canadian educational credential on the income growth of racial‐minority recent immigrants compared to native‐born Canadians. The results indicate that the earnings gap between recent immigrants and native‐born Canadians is significantly reduced with the attainment of a Canadian educational credential.
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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