Job Developers' Training and Employer Education for Integration of Internationally Educated Professionals in the Canadian Labour Market
Bibliographic record
Abstract
Job developers promote job seekers including internationally educated professionals to local employers. In order to excel in doing this; they need to be appropriately trained so that they can educate employers about the benefits of hiring internationally educated professionals. In absence of adequate professional training for job developers, government-funded employment agencies need to provide structured on-the-job-training so that job developers become skilled in promoting their clients to employers. This article explores different organizational development ideas, attempts to relate them to a job development framework and suggests that these trainings need to address how to use a sector specific approach. Referring to organizational learning and organizational development concepts, this article establishes that job developers’ trainings also need to apply a data driven, empowering and systems thinking approach as training tools. Denoting the theory of knowledge creation, this article also posits the application of converting tacit knowledge to explicit knowledge and use of research-challenge resistance, resources-rewards approach for educating employers about the benefits of hiring internationally educated professionals.
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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 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".