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Record W4286447990 · doi:10.1111/ecpo.12225

“Restrict foreigners, not robots”: Partisan responses to automation threat

2022· article· en· W4286447990 on OpenAlex
Nicole Wu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomics and Politics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Toronto
FundersHorace H. Rackham School of Graduate Studies, University of Michigan
KeywordsTechnological changeEconomicsResistance (ecology)GlobalizationDisplacement (psychology)ImmigrationDiversity (politics)ScholarshipWageLabour economicsPolitical economyPolitical scienceMarket economyEconomic growthPsychologyLawMacroeconomics

Abstract

fetched live from OpenAlex

Abstract Recent scholarship on technological change highlights its negative impacts on employment and wages. However, a decade of nationally representative surveys show that Americans hold favorable views toward technology despite concerns over labor displacement. How do people cope with employment threats from a trend they consider desirable? Using a survey experiment, this paper argues that people opt to buffer domestic workers from technological threats with substitute policies against outgroups that they believe could improve wages and employment prospects. Specifically, direct cues about technological displacement make Republicans more likely to demand tighter restrictions on immigration and Democrats more likely to support higher tariffs. In other words, citizens respond to automation anxiety by blaming and penalizing groups that they consider unwelcome or objectionable, depending on their partisanship. Respondents remained reluctant to express support for technological restrictions. Thus, automation anxiety may intensify resistance to globalization, but not necessarily technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.327
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it