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Record W6986658605

Public attitudes towards immigration in Canada: Decreased support and increased political polarization

2024· other· en· W6986658605 on OpenAlexaboutno aff

Bibliographic record

VenueEconstor (Econstor) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPolarization (electrochemistry)PoliticsPublic opinionBiology and political orientationIdentification (biology)Association (psychology)
DOInot available

Abstract

fetched live from OpenAlex

We explore the evolution and determinants of attitudes towards immigration in Canada, utilizing Canadian Election Studies surveys from 1988 to 2019. Our analysis indicates a notable trend: a consistent decrease in anti-immigrant sentiments until the mid-2000s, followed by a shift around 2008 towards gradually more negative attitudes towards immigration. To better understand the factors influencing these attitudes, we examine a comprehensive set of variables. While economic factors seem to have some association with these attitudes, our findings more significantly underscore the role of group-level socio-psychological factors. Additionally, our analysis identifies an emerging polarization along political party lines beginning around 2006. Assessing the relative impact of these factors, our analysis suggests that political party identification has become increasingly significant in influencing attitudes toward immigration.

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)0.002
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 score0.024
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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