Bibliographic record
Abstract
The article is aimed at defining the main factors of Canada’s immigration attractiveness. The article presents the dynamics of the number of immigrants living in Canada during the 1990-2020 years, the dynamics of immigration to Canada, the main countries of origin of immigrants, as well as the change in the percentage of persons born abroad in the total population of Canada. It is determined that the Canadian migration system is based on the priority of attracting human capital to the country, and most Canadian immigrants are selected according to the points system, which accepts people with the skills that will contribute to the development of the economy. The main programs under which Canada accepts migrants for permanent residence are considered: express entry, provincial program, family migration program, visa program for startups, carers program. The dynamics of average hourly wages in Canada, dynamics of consumer price index are provided. For the period 2010-2020 consumer price index in Canada grew moderately, which is comfortable for both consumers and entrepreneurs. Also Canada’s position in international rankings influencing the choice of the country of residence by migrants is studied. Thus, in 2021, Canada took the twentieth place in the ranking according to the Quality of Life Index; 6th place – according to the Social Progress Index and the Prosperity Index, tenth place – according to the Global Peace Index. Canada ranked 16th in the latest Human Development Index. According to the Migrant Integration Policy Index in 2020, Canada has become the fourth country in the world with the best immigration policy. The main factors of Canada’s immigration attractiveness are defined, among which can be highlighted the following: liberal migration policy; high GDP per capita; rising average wage rates and a low inflation; consistently high positions in the world rankings, which are important indicators of the level of development of the country and comfortable life.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".