Attitudes toward artificial intelligence (AI) and globalization: Common microfoundations and political implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 | 0.012 |
| 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 it