Bibliographic record
Abstract
About 20% of Canadians work in regulated occupations. On average, regulated occupations are expected to provide higher pay because they generally require a high level of education and/or training, and the regulations governing access to these occupations tend to restrict entrance into them. Canada’s immigration policy favors immigrants with high educational backgrounds, which can lead to working in regulated occupations. About 60% of Canadian immigrants fall into the economic category (skilled workers and business immigrants), as opposed to the family reunification and refugee categories. Access to regulated occupations, is directly linked with the issue of foreign credential recognition – a widely-recognized disadvantage for immigrants in the labour market. Estimates of the loss to the Canadian economy from the underuse of immigrant skills vary between $2 and $5.9 billion each year. The economic position of immigrants in Canada is well documented. However, the number and proportion of immigrants working in regulated and unregulated occupations is unknown. A study by CLSRN affiliates Magali Girard (University of Montreal Hospital Research Centre) and Michael Smith (McGill University) entitled “Working in a Regulated Occupation in Canada: An Immigrant – Native Born Comparison†* tries to determine whether individuals with foreign education are indeed less likely to be employed in a regulated occupation than someone with domestic credentials, and if so, what are the factors that influence this situation. Pre-immigration human capital’s portability and relevance in the host country are important contributing factors to the earning outcomes of recent immigrants in many developed nations. Given Canadian immigration policy’s emphasis on attracting highly-educated individuals, immigrants to Canada are very likely to have worked in high-skilled occupations prior to immigrating. Following immigration however, many do not find employment in high skill occupations. In a paper entitled: “The Portability of New Immigrants’ Human Capital: Language, Education and Occupational Matching†* CLSRN affiliates Arthur Sweetman (McMaster University), Casey Warman (Dalhousie University) and Gustave Goldmann (Carleton University) examine the implications of human capital portability for new immigrants to Canada for earnings – including interactions between education, language skills and pre- and post-immigration occupational matching. *Previous versions of these papers were released in the CLSRN Working Paper Series.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.547 | 0.375 |
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".