Gone For Good: Deindustrialization, White Voter Backlash, and US Presidential Voting
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
Globalization and automation have contributed to deindustrialization and the loss of millions of manufacturing jobs, yielding important electoral implications across advanced democracies. Coupling insights from economic voting and social identity theory, we consider how different groups in society may construe manufacturing job losses in contrasting ways. We argue that deindustrialization threatens dominant group status, leading some white voters in affected localities to favor candidates they believe will address economic distress and defend racial hierarchy. Examining three US presidential elections, we find white voters were more likely to vote for Republican challengers where manufacturing layoffs were high, whereas Black voters in hard-hit localities were more likely to vote for Democrats. In survey data, white respondents, in contrast to people of color, associated local manufacturing job losses with obstacles to individual upward mobility and with broader American economic decline. Group-based identities help explain divergent political reactions to common economic shocks.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| 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