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Speaking Out on Immigration Policy in Australia: Identity Threat and the Interplay of Own Opinion and Public Opinion

2010· article· en· W2139094650 on OpenAlexaff
Winnifred R. Louis, Julie M. Duck, Deborah J. Terry, Richard N. Lalonde

Bibliographic record

VenueJournal of Social Issues · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsYork University
Fundersnot available
KeywordsPublic opinionImmigrationPerceptionPoliticsNormativePolitical scienceSocial psychologyIdentity (music)SilenceResistance (ecology)SociologyPsychologyLaw

Abstract

fetched live from OpenAlex

This article presents a survey of 667 Australian voters examining support for a new conservative social movement in relation to attitudes toward Asian immigration, involvement in an evolving anti-immigration debate, and willingness to speak out politically. Supporters of the new conservatives were motivated to get involved and speak out by perceived threat to White Australians, as well as the perception of a favorable normative climate. In contrast, for opponents, higher education and welcoming attitudes toward Asian immigration were associated with political involvement, as well as the perception that the social climate was changing against them (becoming more conservative). The data show that in a time of changing public opinion, people may speak out more when they perceive that their views are losing ground, providing evidence for active resistance rather than a spiral of silence on the part of the losing side.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.411
Teacher spread0.378 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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