The Impact of Economic and Cultural Cues on Support for Immigration in Canada and the United States
Bibliographic record
Abstract
Abstract. Past research suggests that citizens' attitudes toward immigration are driven by perceptions of immigrants' (a) economic status and (b) ethnicity. In this study, we use an online survey conducted with a representative sample of Canadians to test to what extent economic and cultural cues influence support for individual immigrants. In particular, by drawing on a parallel US survey, we explore whether Canadians' relatively unique (positive) attitudes toward immigration make them more immune to economic and cultural threat manipulations than their American counterparts. The analysis is based on an experimental design embedded in a series of immigrant vignettes that vary the ethnoracial background and social status of an individual applying for immigration. We examine overall support for immigration, as well as the extent to which both ethnic and economic status cues affect support for individual immigrants. We also explore variance within Canada, specifically, in Quebec versus the rest of the country. Results offer new and unique information on the structure of attitudes on diversity and immigration in Canada. Most importantly, they suggest the relative importance of economic cues in support for immigration in both countries. Résumé. Divers travaux de recherche ont suggéré que les attitudes des citoyens au sujet de l'immigration sont influencées par leur perception (a) du statut économique et (b) de l'ethnie des immigrants. Afin de tenter de savoir jusqu'à quel point les informations socioéconomiques et culturelles ont effectivement un impact sur le soutien des citoyens envers les immigrants, la présente étude fait usage d'un sondage mené en ligne avec un échantillon représentatif de la population canadienne. En nous appuyant sur un sondage américain similaire, nous cherchons plus précisément à savoir si l'attitude (positive) relativement unique des Canadiens vis-à-vis de l'immigration les rend moins susceptibles d'être manipulés par l'évocation de menaces économiques et culturelles que leurs voisins américains. Notre analyse se fonde sur une expérience utilisant une série de vignettes qui modifient les caractéristiques ethnoraciales ainsi que le statut social d'un individu procédant à une demande d'immigration. Nous examinons non seulement le soutien pour l'immigration en général, mais aussi la mesure dans laquelle les informations relatives à l'ethnie et au statut économique d'un immigrant affectent le soutien que les citoyens lui offrent. Nous étudions aussi la variance à l'intérieur du Canada, plus spécifiquement entre le Québec et le reste du pays. Les résultats ainsi obtenus fournissent de l'information nouvelle et unique ayant trait à la structure des attitudes par rapport à la diversité et l'immigration au Canada. De surcroît, ces résultats suggèrent le rôle relativement important que jouent les informations d'ordre socioéconomique dans le soutien de l'immigration tant aux États-Unis qu'au Canada.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".