Professional Immigrants: Confronting Multiple Barriers to Canadian Employment Opportunities.
Bibliographic record
Abstract
Background: Immigration Partnership Saskatoon (IPSK) received much anecdotal evidence of Internationally trained job seekers unable to find employment in their fields due to lack of Canadian experience and Canadian credentials. Methods/Approach: Research project undertaken to understand the challenges internationally-trained and experienced immigrants face during the registration, certification and licensing processes when trying to find work in their fields, through Saskatchewanʼs regulatory bodies. IPSK created the Employment Resource Guide to help newcomers navigate some of the challenges uncovered in the research. Results/Observation: This inability to meaningfully contribute their expertise in their new home country has led to feelings of being de-valued, distressed, and frustrated. Canadians are deprived of essential services that these professional newcomers can provide. Additionally, the socioeconomic fabric of our province is being depleted as we are unable to benefit from these highly skilled immigrantsʼ knowledge and abilities. Conclusion: These observations pose the question, Are employer/regulator requirements of Canadianbased education, training, experience, and professional eligibility a way of excluding immigrants from opportunities appropriate to their qualifications gained abroad? Further work is needed to shed light on not just generalizations from data, but actual evidence from immigrants; regulators; and Ministries who have data from Provincial Nominee Programs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".