Bibliographic record
Abstract
Immigrants to Canada earn more the longer they live here. Their incomes are also influenced by factors including official language fluency, immigration category, and racial discrimination. In this article we address one factor – access to affordable credit. Immigrants consume almost half of their savings in moving to Canada. This can leave little leftover to pay for recertification or licensing fees. Newcomers with professional accreditations earned outside Canada who cannot recertify or become licensed on their arrival often find work that is not commensurate with their training. This is a loss to both immigrants and those born in Canada who might benefit from their skills. Small, low-interest loans, or “microcredit” may be one way to achieve faster certification. The figure on the right shows data from Windmill Microlending, a registered charity providing microloans to immigrants. Red dots represent the income of an immigrant loan recipients (red) and non-recipients (green) measured against their time in Canada. The lines show the average rise in pay for immigrants the longer they live in Canada. On average, loan recipients receive a 26% increase in income following disbursement and a faster rate of increase in income over time.
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.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".