Why Are Public Attitudes towards Immigration in Canada Becoming Increasingly Positive? Exploring the Factors Behind the Changes in Attitudes towards Immigration (1998–2021)
Bibliographic record
Abstract
Focusing on why and how public attitudes towards immigration in Canada have grown more positive between 1998 and 2021, this paper examines whether the changes in attitudes stem from changes in population characteristics or changes in the effect of these characteristics. Our analysis sheds light on the implications of the 2008–2010 financial crisis on the support for immigration. Our logit regression analysis shows that positive attitudes towards immigration are positively related to higher levels of education attainment regardless of the survey years but negatively associated with the support for conservative political parties, especially during and after the financial crisis. By employing decomposition analysis, we investigate the shift in public opinions across individual characteristics before, during, and after the 2008 financial crisis. We find that, for all periods, most of the attitude shift results from the change in the effect of population characteristics rather than the change in the characteristics themselves.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".