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Record W3131202389 · doi:10.1177/0022002721994085

Trade Rage: Audience Costs and International Trade

2021· article· en· W3131202389 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Conflict Resolution · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsVignetteStatus quoEconomicsTest (biology)Public opinionPresidential systemInternational tradePolitical sciencePublic economicsPoliticsLawPsychologySocial psychology

Abstract

fetched live from OpenAlex

Politicians frequently issue public threats to manipulate tariffs but only sometimes follow through. This behavior theoretically ought to generate audience costs. We therefore test the validity of audience costs in trade war settings through a vignette-based survey experiment. The vignettes describe a hypothetical situation involving the U.S. and a second country (China, Canada, or unspecified) with whom the U.S. has a trade deficit. The president (Democrat, Republican, or unspecified) either maintains the status quo, threatens to impose tariffs and backs down, or threatens to impose tariffs and follows through. Our findings highlight differences between security and trade conflict when it comes to audience costs and presidential approval. While Americans sanction the president for issuing a threat to raise tariffs, they generally support backing down. Regression modeling and text analysis of a free response question from our surveys suggest this is because consumers are wary of paying the costs of tariffs.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.045
GPT teacher head0.365
Teacher spread0.320 · 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