Quebec's Lackluster Performance in Interprovincial Migration and Immigration: How, Why, and What Can Be Done
Bibliographic record
Abstract
In light of the persistent trend in Quebec's diminishing share of the Canadian population (from 28.9% in 1966 to 24.0% in 2000), this paper examines Quebec's roles in the life-time interprovincial migration of the Canada-born elderly, the initial destination choices of landed immigrants, and the post-landing relocations of immigrants. From the 1996 census data, we found that Quebec ended up with an overall net loss of 4.9% of Canada-born elderly lifetime migrants, which was highly selective of non-Francophones and moderately selective of those with better education and higher income. The selective net loss was rooted in the cultural disharmony between Quebec and the rest of Canada as well as Quebec's relative shortage of good economic opportunities. From the micro data on the landed immigrants, we found that Quebec attracted only 16.6% of Canada's 1980-99 landed immigrants, and that its share was highly selective by immigration class, language ability, and place of birth. Although the Quebec government got the exclusive responsibility for the selection of its independent immigrants since 1991, we have identified serious internal and external obstacles and constraints that prevent Quebec from getting enough immigrants to match its population share in the foreseeable future. From the 1980-95 data of linked records of landing and tax-filing, we found that the post-landing relocations of immigrants resulted in Quebec's net loss of 13% in three years after landing, and that the net loss was also highly selective by immigration class, language ability, and place of origin, but fortunately did not result in a larger net loss of the best educated immigrants. Some reasons for the selective losses were the non-French-speakers' difficulty in adjusting to the French milieu, the business immigrants' use of Quebec as a stepping stone to the rest of Canada, and the desire of most immigrants to have their children educated in English schools. In our opinion, the fundamental way to increase Quebec's share of new immigrants and to reduce its net losses of Canada-born and foreign- born migrants is to soften its restrictive language regulations and to help Quebeckers cultivate positive views on immigration. Adoption of foreign children, recruitment of third-world students and researchers in the universities and research institutes in French-speaking countries, and invitation of non-Francophone students from the rest of Canada to study in the French schools of Quebec are some viable options that can help reduce Quebec's loss in population share in the long-run without threatening its Frenchness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".