Dossier: Mexican Migration to Canada Statistical Data and Interview With Chona Iturralde, Citizenship and Immigration Canada ( CIC )
Bibliographic record
Abstract
Canada was built on immigration, and one of every six Canadian residents was born outside the country (McCrank, 2010). Canada’s immigration may be compared to the United States’ due to geographical proximity and similar historical conditions, and to Australia’s, because of similar geographic conditions (huge resources and territories, but small populations). Canada, the U.S., and Australia are all principal receiving countries in international migration that accepted a large number of displaced persons and refugees for permanent settlement after World War II. Nevertheless, the rate of immigration per capita to Canada is higher than the one to the U.S. (Green, 1995). Canada also has a stricter policy for selecting candidates, especially since the introduction of the points system (the Skilled Worker Programme) in 1967 that evaluates migrants according to their education, language skills, work experience, age, arranged employment, and adaptability. Canada currently receives more than 300 000 applications a year (Cerna, 2010), but only accepts about 250 000 immigrants and 175 000 foreign temporary workers annually (McCrank, 2010). Even during the 2008 recession, Canada did not restrict entry of new migrants. The Canadian government’s rationale was that, given the demographic changes expected over the next 20 years, “developed countries need to attract talent, reduce skills gaps and project the skills shortage for the next few years. This is the time to attract the best skills instead of reducing the intake of immigrants” (Mittal, 2008). Minister for Immigration Jason Kenney stated that the country “would maintain its current policy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".