Theoretical analysis of the Manitoba Provincial Nominee Program and the Francophone Immigration Strategy as policy interventions to attract highly skilled immigrants to Manitoba
Bibliographic record
Abstract
In the era of globalization, Canada is one of many industrialized countries involved in the worldwide race for talent (McHale 2003). Thus, Canada plans to accept 401,000 immigrants by 2021 by giving preference to economic immigrants to strengthen the country’s middle class (IRCC, 2020e). Ontario, Quebec and British Columbia are the top intended destinations of newcomers (Statistics Canada 2010; IRCC 2015; IRCC 2018). Consequently, smaller provinces do not fully benefit from immigration. Equal distribution of immigrants among provinces and territories is a key issue in Canada. Since immigration is a shared responsibility, there are policies designed to address it in both federal and provincial levels. This research is focused on two such programs: the Manitoba Provincial Nominee Program (MPNP) and the Francophone Immigration Strategy. The MPNP is based on federal-provincial agreements to meet Manitoba-specific labour-market needs. The second initiative is designed to enhance vitality of French-speaking minority communities outside Quebec through immigration (IRCC 2019). By focusing on the province of Manitoba, this study uses new institutionalism and nudge theory to examine both programs that aim to attract highly skilled immigrants to the province. The research methodology is mainly qualitative as it applies a case study approach and uses thematic analysis of government documents related to both immigration programs. Through this analysis we can learn how the theories help us understand Manitoba’s immigration programs and make useful policy recommendations. This combination of innovation and learning can be a powerful tool for developing effective policies to build a better future for Canadian- and foreign-born peoples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".