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Record W2504140489 · doi:10.1177/2332649216660117

The Effects of Perceived Threat, Political Orientation, and Framing on Public Reactions to Punitive Immigration Law Enforcement Practices

2016· article· en· W2504140489 on OpenAlexaboutno aff
Joshua Woods, Agnieszka Marciniak

Bibliographic record

VenueSociology of Race and Ethnicity · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsVignetteImmigrationFraming (construction)Punitive damagesPoliticsPolitical scienceEnforcementBiology and political orientationLawCriminologyOfficerPsychologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

This study explores variation in people’s reactions to a punitive immigration law enforcement practice. Using a vignette-styled framing-effects experiment, we examined whether reactions to the practice depend, in part, on who receives its consequences. More than 500 undergraduates from a large Mid-Atlantic university read a brief vignette about an immigrant motorist who is stopped by a police officer for a broken taillight violation and then detained for failing to document his legal immigration status. We manipulated three characteristics of the motorist in the vignette, including his nationality (Mexico/Canada), occupation (factory worker/software engineer), and documentation status (documented/undocumented). When we framed the motorist as an unauthorized immigrant, the subjects were more likely to condone the officer’s intrusive actions. We also found that the subjects’ political orientation and immigrant threat perceptions were powerful predictors of their normative reactions to the vignette.

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.

How this classification was reachedexpand

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)0.002
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 score0.791
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.040
GPT teacher head0.394
Teacher spread0.353 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2016
Admission routes1
Has abstractyes

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