Asymmetric information, credential assessment services and earnings of new immigrants
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
Based on the 2016 Canadian Census of Population, some immigrant groups have higher entry-earning returns on their ability than others, and experience a lot more variation in earnings given similar variations in ability compared to other groups. The uneven variance in earnings given similar variances in ability is an indication of statistically discriminated immigrant groups due to information gaps. I show that credential assessment is an essential service to reduce information gaps between employers and immigrant workers. While assessors do not reveal an immigrant worker's true ability without error, they may supply contextual and/or specific information about the worker and their source country. The more about the source country that goes unexplained, variance in ability and immigration increases, while variance in earnings decreases. However, these results are generated only if the credential assessor faces considerable difficulty in learning about the source country and migrants are of low ability.
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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) | 0.000 | 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