The impact of immigration on local ethnic groups' demographic representativeness: The case study of ethnic French Canadians in Quebec
Bibliographic record
Abstract
Abstract The purpose of this research was to investigate the impact of immigration on local ethnic groups' demographic weight (DW) by presenting a case study. In this study, the ethnic French Canadians (EFC), a group that makes up the majority of the Province of Quebec, were studied to evaluate the impact of immigration on their DW. It was found that EFC transitioned from a DW of 79% in 1971 to a DW of 64.5% in 2014; projections predict that EFC would decrease to a DW of 45% in 2050. Moreover, 45 immigration rate scenarios and total fertility rates were projected; it was found that immigration level and fertility level could be jointly classified into three categories related to their effect on ethno‐demographic decrease; one of these categories may help suggest a quantitative definition for the concept of mass immigration .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".