Bibliographic record
Abstract
The article is devoted to the identification of transformational changes in Canada’s migration policy in the current geopolitical and socio-economic conditions. The origins of the formation of the state migration policy of Canada were identified, and statistical indicators on the number of immigrants in Canada in 2000-2020 were analyzed. It is established that in 2023, the vast majority (97.6 %) of Canada’s population growth came from international migration (both permanent and temporary immigration) and the remaining portion (2.4 %) came from natural increase. The structure of the plans for immigration levels in Canada for 2023-2026 is studied. Overall, the Immigration Levels Plan 2024–2026 has permanent resident admissions targets of 485,000 in 2024 and 500,000 in 2025. For 2026, the plan is to stabilize at the 500,000 level. The components of the strategic approach to immigration regulation are summarized; the categories of immigrant admission in Canada, as well as the main state immigration programs are characterized. It is concluded that Canada’s current migration policy is focused mainly on business migration. The author identifies the comprehensive tools for regulating immigration that have been operating in Canada for many years. The author highlights the current trends in the regulation of state migration flows that have emerged over the past few years (active promotion of digital transformation, increased competition for talent, revision of immigration goals in the context of determining certain restrictions). The article contains a proposed list of the main components of Canada’s modern migration policy (focus on long-term economic growth; support for humanitarian traditions; support for Francophone immigration; continued implementation of new digital systems in the field of migration regulation). Conclusions are drawn that it is expedient to further study the current transformational changes in Canada’s migration policy in the context of finding effective measures and instruments for regulating migration flows on the principles of long-term and complexity. Keywords: Canada, immigrants, international migrants, labor market, migration, migration policy, transformation.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".