Bibliographic record
Abstract
Canada, which has traditionally welcomed immigrants, has remained strongly proimmi gration.This is reflected in policies mandating comparatively high immigration levels and in the fact that public opinion generally supports it.Clearly this makes the country an exception to prevailing attitudes about this issue across most in dustrial nations, attitudes that have received much attention, particularly in the United Kingdom, the United States, France, and the Netherlands.This "Canadian exceptio nalism" on immigration is reflected in cross-national comparisons of public opinion, most recently by the German Marshall Fund (2010, 7), which also indicated that Canadians were more likely to see immigration as an opportunity than as a problem.What accounts for the generally quite positive Canadian approach to this issue?Why have anti-immigrant views such as have been seen in other countries not become more prominent in Canada?Are there indications that Canadian attitudes might turn in a more negative direction in the future?To address these questions, this chapter examines available Canadian public opinion data, including a recent national opinion survey, to attempt to clarify the social roots of popular support for high immigra tion levels in Canada.Canadian immigration levels, strong throughout the nation's history, have been particularly high for the past 20 years, when Canada has received about 250 000 permanent immigrants annually, representing between 0.7 and 0.8 percent of the total population.As a result of relatively high immigration, the Canadian po pulation has a substantially greater foreign-born component compared to the United States and most European countries (United Nations 2006).Much of this immigra tion has been concentrated in the major cities of Toronto, Montreal, and Van couver, and in the recent period, Toronto alone has received nearly100 000 new immigrants each year, making it one of the world's most immigrant-intensive large cities.In this context of high immigration, it is particularly remarkable that there has been such widespread acceptance of and support for it in Canada, with relatively little of the acrimonious debate seen elsewhere.Public opinion polls show that almost
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.367 |
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".