Which Human Capital Characteristics Best Predict the Earnings of Economic Immigrants
Why this work is in the frame
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Bibliographic record
Abstract
While an extensive literature examines the association between immigrants' characteristics and their earnings in Canada, there is a lack of knowledge regarding the relative importance of various human capital factors, such as language, work experience and education when predicting the earnings of economic immigrants. The decline in immigrant earnings since the 1980s, which was concentrated among economic immigrants, promoted changes to the points system in the early 1990s and in 2002, in large part, to improve immigrant earnings. Knowledge of the relative role of various characteristics in determining immigrant earnings is important when making such changes. This paper addresses two questions. First, what is the relative importance of observable human capital factors when predicting earnings of economic immigrants (principal applicants), who are selected by the points system? Second, does the relative importance of these factors vary in the short, intermediate, and long terms? This research employs Statistics Canada's Longitudinal Immigration Database (IMDB).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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