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Record W2035112500 · doi:10.1177/0020702013518177

Transatlantic convergence? The archaeology of immigrant integration in Canada and Europe

2014· article· en· W2035112500 on OpenAlexaffabout
Keith Banting

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsQueen's University
Fundersnot available
KeywordsMulticulturalismConvergence (economics)ImmigrationDiversity (politics)Divergence (linguistics)Political scienceEuropean integrationEuropean unionPolitical economyCultural diversitySociologyDevelopment economicsLawEconomic growthEconomicsInternational trade

Abstract

fetched live from OpenAlex

At first glance, Canada and Europe seem to be diverging dramatically in their approach to immigrant integration. While support for a multicultural approach seems to remain strong in Canada, a potent backlash pervades European debates. This paper argues that beneath the image of transatlantic divergence, there are important elements of convergence. First, the retreat from multiculturalism in Europe is more complete at the level of discourse than policy. With a few notable exceptions, multicultural policies have remained stable or even grown stronger since 2000. In many countries, new integration programs are being layered over multicultural initiatives introduced in earlier decades. Second, many of the new integration policies celebrated as evidence of a U-turn away from multiculturalism resemble programs that have long been part of immigrant integration in Canada. As a result, transatlantic convergence is indeed part of the contemporary story. However, there are also limits to this convergence. While some European countries are opting for liberal, voluntary approaches to integration, which can be combined with a multicultural approach to diversity, others are adopting more obligatory, illiberal versions of civic integration that seem inconsistent with the support for diversity central to a multicultural approach.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.104
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.278
Teacher spread0.270 · 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 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

Citations30
Published2014
Admission routes2
Has abstractyes

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