MétaCan
Menu
Back to cohort
Record W4402665995 · doi:10.1111/ajps.12916

Authoritarian cue effect of state repression

2024· article· en· W4402665995 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.

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

Bibliographic record

VenueAmerican Journal of Political Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychological repressionAuthoritarianismState (computer science)PsychologySocial psychologyPolitical scienceChemistryComputer scienceBiochemistryPoliticsDemocracyLaw

Abstract

fetched live from OpenAlex

Abstract State repression in autocracies has long been assumed to elicit explicit or implicit disapproval from citizens. Recent studies suggest that authoritarian governments can garner support for repressive policies through active information manipulation or exploiting social cleavages. However, is it possible for citizens to support repression even without government manipulation? We propose the “authoritarian cue effect,” arguing that citizens’ attitudes toward state repression can be endogenously shaped by instances of state repression, which may be interpreted as cueing messages signaling the regime's disapproval of the punished behaviors. Using a novel belief correction survey experiment, we empirically demonstrate that state repression can induce the public to pick up on cues and automatically adopt the state's stance, perceiving repressed behavior as having more negative externalities and supporting state repression more. This cue effect suggests that authoritarian state repression can self‐legitimize and evade public opinion backlash in a less costly manner than previously presumed.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.370
Teacher spread0.361 · 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