Love Thy Neighbo(u)r? Political Attitudes, Proximity and the Mutual Perceptions of the Canadian and American Publics
Bibliographic record
Abstract
Abstract There has been renewed interest in recent years in both the foreign perceptions of the United States as well as the foreign policy attitudes of the American public. In this light, it is interesting to observe that there is a substantial body of research on Canadian public opinion toward the United States but relatively little on American public opinion toward Canada. Further, most literature neglects the effect of spatial proximity to the other country on perceptions. This article addresses both shortcomings in the literature. It investigates the mutual perceptions of the Canadian and American publics drawing on public opinion data from both Canada and the US. The explanation of attitudes toward the other country has three main foci: the roles of political party identification and political ideology; the role of spatial proximity to the Canada–US border; and the interactive relationship between political attitudes and border proximity.
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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 | 0.003 | 0.005 |
| 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 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| 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".