Professional mobility between France and Quebec. Understanding the role and pertinence of mutual recognition agreements.
Bibliographic record
Abstract
Mutual recognition arrangements (MRAs) between France and Quebec are governed, in Quebec, by the regulatory bodies that oversee quality of professionals’ services. These professional orders have responsibilities over initial certification, skills recognition and ongoing maintenance of benchmark quality standards of professional practice. Once concluded, each MRA has then been implemented through regulations that define eligibility and specific scenarios of complementary training and/or successful examination. Therefore, professional orders navigate between legal obligations linked to public protection and political and economic pressures to promote transnational mobility. \nWe will discuss here results from case studies among engineers, lawyers, doctors, nurses and pharmacists. Various factors contribute to shape the course of entry into regulated professions by French immigrants in Quebec. Namely, local job market has various attractive or repulsive aspects, linked to each profession’s comparative practice and treatment in France and Quebec. MRAs are used for various reasons, relating to each profession’s differing characteristics in France and Quebec: work contract, mobility, transnational opportunities, etc. French professionals in Quebec face new ideas and activities inside their profession, as well as propose changes in their profession’s practice and professionalism in the host society. During the study, we observed various trends of professional development initiated by these new arriving professionals (e.g. new professional associations, social media, and perception of the profession). Analysis leads to comment on key aspects of the governance of these MRAs, through their design and implementation: pertinence, awareness of practical conditions, and role among professionals through the host society’s perspective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".