Bibliographic record
Abstract
At the 2004 Law and Diversity Conference in Toronto on the accreditation of foreign-trained immigrants in Canada, speaker Naomi Alboim called Canadian immigration policy “one of seduction and abandonment.” Seduction because skilled workers are selected as immigrants based on their high levels of education and experience, which leads them to expect that they will be able to apply these skills and experience in the Canadian labor market. Abandonment because, once in Canada, the immigrant workers receive little help with the accreditation of their education and professional certification, preventing them from applying their skills. Immigrants in regulated trades and professions such as the electrical trade, engineering, law, medicine, nursing, and teaching often lose access to the occupations they previously held—an effect commonly known as “deskilling.” The abandonment of immigrants is not simply the result of inadvertent neglect and the failure of policy. It can also be interpreted as a systematic process of distinction and subordination. By excluding many skilled, foreign-trained immigrants from high-status occupations in Canada, the regulation of educational and professional credentials enables domestic-educated workers to dominate these occupations. The level of education among Canadian immigrants has steadily increased since the 1950s (Akabari 1999). Nevertheless, immigrants have failed to benefit from their educational attainments and have lower returns on their education than Canadian-born workers (Reitz 2001a, 2001b). Level of education, in fact, fails as an accurate predictor of labor market performance among immigrants (E. N. Thompson 2000). Similarly, the benefits immigrants receive for foreign work experience have deteriorated. In the 1960s, one year of foreign work experience was rewarded with an average 1.5 percent increase in earnings for immigrants. By the late 1990s, this wage increase dropped to only 0.3 percent (Statistics Canada 2004: 5). Furthermore, skilled immigrants require an increasing amount of time to catch up with the wages of Canadian workers with similar skills and education, if they catch up at all (Ley 1999). These national trends also apply to immigrants in Vancouver. Three-fourths of all immigrant professionals from India who settled in Vancouver experienced occupational downward mobility after their arrival in Canada.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".