Numbers and Images: Representations of Immigration and Public Attitudes about Immigration in Canada
Bibliographic record
Abstract
Abstract Perceptions of numbers (numerical estimations of migrant flows) and mental images (beliefs about characteristics and motives of immigrants) have been shown to be important predictors of cross-national immigration attitudes. However, this finding has seldom been verified in Canada. As a result, we know little about how Canadians estimate the amount and type of migrants coming into the country, what drives the generation of these numbers and images, and what the consequences of numerical estimations and mental images of immigration are for public attitudes toward immigration. Using nationally representative cross-sectional survey data from 2019, this article reports that Canadians generally overestimate the number of refugees and asylum-seekers coming into the country but are comparatively less prone to overestimating the overall number of immigrants. Canadians also rely on mental images about the reasons for immigrating to Canada that diverge from the realities of Canada's immigration program. We document how reliance on these numbers and images is driven by the type of media consumed, feelings of threat, and individual-level characteristics of Canadians. In doing so, this article demonstrates that mental images strongly influence Canadians’ attitudes toward immigration; numerical estimates also matter, but less so. Furthermore, perceptions of the number of migrants arriving affect latent preferences toward immigration—such as ethnocentrism, perceptions of “threat,” and border insecurity—while mental images shape both preferences for lowering immigration intake and latent preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".