Attitudes toward artificial intelligence (AI) and globalization: Common microfoundations and political implications
Bibliographic record
Abstract
Abstract Advances in artificial intelligence (AI) are reshaping labor markets and sparking political debates. Like economic globalization, AI developments promise benefits, including job creation and lower prices, but also costs such as job displacement, raising crucial questions about public perceptions. Will AI, like globalization, challenge existing paradigms and trigger a backlash? Leveraging a conjoint experiment with 6,000 respondents from the United States and Canada, we examine public opinion toward offshoring and generative AI, focusing on the multidimensional trade‐offs between job and price changes. Across all scenarios, respondents are equally or more sensitive to price changes than employment shifts. AI is favored over offshoring, especially among Democrats, highlighting an emerging partisan divide in the United States. Republicans and Canadians show more varied support, indicating AI is not immune to opposition. By focusing on the microfoundations of opinion formation, we identify scenarios that may trigger or temper protectionist stances.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".