How Alternative Justifications for Automation Fuel Public Support for Redistribution, Protectionism, and Immigration Restrictions
Bibliographic record
Abstract
How does automation affect preferences for redistribution, protectionism, and immigration restrictions? A growing literature demonstrates how labour disruptions caused by automation fuel popular support for far-right political parties, can increase favourability toward redistributive or protectionist policies, and even cause citizens to blame foreigners for job losses. Notwithstanding these contributions, prior work often obscures the many trade-offs associated with automation. Automation can also increase the competitiveness of domestic firms in global markets, increase firm profitability, and help address labour shortages. In this project, we evaluate how information about the different effects of automation shape citizens’ views toward redistribution, protectionism, immigration, and automation more generally. Empirically, we use a vignette experiment and vary whether automation is linked to economic nationalism, economic inequality, and immigration. Moreover, fielding the experiment in both Japan and Canada offers evidence for the generalizability of the findings while also highlighting how important variation in contextual factors (such as the domestic immigration regime) might shape the effects of different justifications. This project thus aims to broaden our understanding of the political economy of automation and the impacts of automation on policy preferences.
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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 | 0.006 | 0.018 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".