"Canadian Experience' and Other Barriers to Immigrants' Labour Market Integration: Qualitative Evidence of Newcomers From the Former Soviet Union
Bibliographic record
Abstract
Employment has always been the primary settlement need for most newcomers. However, more recent immigrants’ labour market integration achievements have generally not matched that of the Canadian-born, despite the fact that, on average, immigrants arrive in Canada better educated and at a similar stage of their career as those born in the country. Lack of recognition of international credentials, insufficient language proficiency and lack of Canadian experience are the most commonly cited barriers to immigrants obtaining employment commensurate with their skills level. This puts immigrants in a classic Catch 22 situation: unable to gain appropriate employment without Canadian experience, but unable to get this experience. As a result, many highly-skilled immigrants spend years trying to break into the skills commensurate labour market, and the longer it takes, the more difficult it becomes to have their skills and experience recognized. This study was designed to identify the nature and scope of the barriers that prevent foreign-trained professionals from practicing their professions and contributing more meaningfully to their new society. In particular, the study seeks to explore experiences of main applicants who came to Canada under the Skilled Workers category from the republics of the former Soviet Union.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".