Replication Data for: Causal Beliefs and the Potential for Political Backlash Against AI
Bibliographic record
Abstract
Artificial intelligence is poised to reconfigure the economy and politics. Although new technologies often produce net economic gains, their costs and benefits are unequally distributed, making them susceptible to politicization. We argue that whether and how AI becomes mobilized for partisan gain will depend on the public’s causal beliefs about the winners and losers of AI. We categorize these causal beliefs into four types using a novel survey instrument fielded with approximately 6,000 Americans and Canadians. Using latent class analysis, we show that while some respondents are supportive, a significant portion of the public theorizes AI to be a threat—harming consumers and replacing rather than complementing workers’ skills. These beliefs are already aligned with political preferences, predicting support for policies that delay job loss over those that help workers adapt, and polarizing voters along existing partisan lines. We conclude that fissures in the public’s attitudes toward AI already exist and can be channeled toward politics.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.128 | 0.066 |
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".