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Record W2777214317 · doi:10.1177/1948550617746463

Changes in the Positivity of Migrant Stereotype Content

2017· article· en· W2777214317 on OpenAlexaffabout
Danielle Gaucher, Justin Friesen, Katelin H. S. Neufeld, Victoria M. Esses

Bibliographic record

VenueSocial Psychological and Personality Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsWestern UniversityUniversity of ManitobaUniversity of Winnipeg
Fundersnot available
KeywordsIdeologySystem justificationStereotype (UML)Social psychologyPsychologyPower (physics)Competence (human resources)PoliticsMigrant workersContent (measure theory)Political science

Abstract

fetched live from OpenAlex

Complementing well-established antecedents of anti-migrant opinion (e.g., threat), we investigated how system-sanctioned ideologies—that is, the collection of beliefs and values espoused by the government in power—are linked with migrant stereotypes. Using Canada as a case study, across three waves of national survey data ( N = 1,080), we found that system-sanctioned pro-migrant ideologies corresponded with (relatively) more positive migrant stereotype content (i.e., increases in perceived warmth and competence). Moreover, controlling for other political ideologies, increases in migrant stereotype positivity were linked to people’s motivation to justify their sociopolitical systems, suggesting that system-sanctioned ideologies may be especially likely to influence the positivity of migrant stereotypes when people are motivated to justify their sociopolitical systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.280
GPT teacher head0.460
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations36
Published2017
Admission routes2
Has abstractyes

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