Employment facilitation programs for professional immigrants in the Greater Toronto area: surveying participants' opinions about the programs
Bibliographic record
Abstract
In the context of the aging Canadian labour market and a growing shortage of trained professionals, the social and economic consequences of skilled immigrants working as low skilled labourers is significant. Calling for the provision of more language-training, job-search and self-marketing training programs for newcomers are among the widely proposed solutions that so far dominate the response system in Canada. The view that seeks a "training solution" presumes that the problem is caused primarily by "skill-gaps" between immigrants' skills and job-market requirements, while research as well as anecdotes in the press have long documented the influence of structural, institutional and cultural factors as well. Surveys, including scaled rating, open-ended response format and post-survey conversation with participants were deployed to investigate the effects of employment training programs on the surveyed sample. The interplay of my personal experience with the developing results raised my awareness of the insufficiency of a traditional research approach (testing a hypothesis through observable data) in explaining differences between the employment status of immigrants with almost the same level of personal skills. Opinion surveys of over 200 graduates from nine different EFP programs in the Toronto area confirmed that while the trainees' job-hunting skills improved, the improved skills did not affect their hope of finding relevant jobs. Similarly, my post-survey participants also thought that a lack of connectedness to professionals and Canadian workplaces hindered their access to professional jobs more than gaps in job-hunting skills. Building on Livingstone's argument about the structural causes of under-employment, I suggest that lowering the growing rate of under-employment among skilled immigrants requires a strategic change from the current skill-training focus to collaborative programs to find workplace opportunities for newcomers to enable them to re-connect to their professions and test their own professional capabilities while being tested in a realistic work settings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".