Issue-Areas, Sovereignty Costs, and North Americans’ Attitudes Toward Regional Cooperation
Bibliographic record
Abstract
Abstract Studies of public opinion toward regionalism tend to rely on questions regarding trade integration and specific regional organizations. This narrow focus overlooks dimensions of regionalism that sit at the heart of international relations research on regions today. Instead, we argue that research should explore public preferences with respect to regional cooperation in different issue-areas. We find that people's views of regional cooperation in North America diverge from their attitudes toward trade integration alone. Using data from Rethinking North America, an untapped public opinion survey conducted in Mexico, Canada, and the United States in 2013, we show that although country-level attitudes toward trade integration in North America were similar, preferences for regional cooperation varied by country depending on the issue at hand. We propose that attitudes are shaped by citizens’ perceptions of the asymmetric patterns of national-level benefits and vulnerabilities created by regional cooperation. Generally, respondents favor cooperation where their state stands to gain greater capacity benefits and oppose it where cooperation imposes greater costs on national autonomy. For policymakers, this multifaceted approach to regionalism sheds light on areas where public preferences for regional cooperation might converge. Future research that disaggregates various aspects of support for regional cooperation should help integrate the study of public opinion with “new” and comparative regional approaches that emphasize the aspects of regionalism beyond trade and formal institutions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".