NAVIGATING PUBLIC SENTIMENT: A COMPARATIVE ANALYSIS OF IMMIGRATION POLICY PERCEPTIONS IN CANADA AND THE UNITED STATES (2017-2020)
Bibliographic record
Abstract
This paper examines the comparative perceptions of immigration policy in Canada and the United States from 2017 and 2020, focusing on how demographic factors such as age, sex, education, and occupation influence public attitudes. Utilizing data from the seventh wave of the World Values Survey, the study employs multinomial logistic regression models to interpret predicted probabilities. The findings reveal that women, younger individuals (18-29 years), and those with higher education levels generally exhibit more positive attitudes towards immigration policies. In contrast, older individuals (50 and over) and those in certain occupational categories, such as skilled and unskilled workers, tend to favour more restrictive policies. Canada has an overall more positive inclination towards immigration policy compared to the United States. The study underscores the importance of job availability and economic opportunities in shaping public sentiment toward immigration. By providing a nuanced understanding of these factors, the research aims to inform the development of balanced and inclusive immigration policies in both Canada and the United States. This work contributes to the broader discourse on immigration by highlighting the complex interplay of demographic variables in shaping public opinion.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".