A talent management future for economic immigration in Canada: building on best practice diversity/inclusion and intercultural competence training
Bibliographic record
Abstract
One of the key questions regarding the integration of economic immigrants into Canadian labour markets is the role that employers will play, especially given the forthcoming changes that will formalize Expressions of Interest (EOI) as intrinsic to the selection process. Immigrants are not a social or financial burden, but as is proven through self-reported hiring practices and projections of many industry-leading employers, a hugely important investment that adds layers of value to workforces in Canada. This enthusiasm to hire foreign-trained professionals is not matched by an enthusiasm to fund the development of their skills, particularly 'soft' skills such as cultural competency and teamwork, in a context that is alien to them. To explore the potential for expanding programs that optimize the performance of multicultural workplaces, address regional labour shortages with targeted immigration, and accelerate the role of talent management in the profile of human resources departments, this study is a demonstration of potential in Canada for a much more integrated, cross-sector, solution-focused economic immigration strategy.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".