Bibliographic record
Abstract
Quebec's historical experience in the field of immigrant integration has always been relatively positive, and mass immigration has received significant support among the population. The special Quebec model of immigration policy deserves international attention and is relevant for study. However, the success of the management of immigration processes is only partially related to policy, and this cannot simply be transferred to another context, other countries, so the issue needs to be considered more broadly. So, the article examines the economic and social integration of immigrants in Quebec, the impact of immigration on social cohesion in the province, the economic situation for new immigrants and the second generation (their children), as well as the relevance of Quebec society discrimination and racism, the general attitude of Quebec residents to the immigration situation in the province. Having studied the specifics of integration policy in Quebec, we will try to conclude whether this policy is effective enough, whether it has some shortcomings, if so, what exactly and how it is possible to correct them. Is the deep and procedurally soft social integration of immigrants one of the results of this policy success what is the level of influence of the policy of multiculturalism on a fairly strong social cohesion? We will also try to find out whether there has been some progress in economic integration, as well as how Quebecians feel about the influx of immigrants to the province and how immigrants themselves evaluate their decision to move to Quebec, and answer the question: why racism or discrimination is minimal in Quebec, compared to the United States and many other European countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".