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Record W7097358142

Becoming Multicultural: Immigrants and the Politics of Membership in Canada and Germany

2016· article· en· W7097358142 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicGerman Colonialism and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMulticulturalismPoliticsGlobalizationImmigration policyColonialismDe facto
DOInot available

Abstract

fetched live from OpenAlex

Canada and Germany are often considered polar contrasts when it comes to immigration and multicultural-ism. Yet in this provocative, insightful, and original analysis of the two nations, Triadafilopoulos observes that the two countries have more in common than may initially be apparent. He notes that that Canada and Germany followed similar trajectories over the 20th century. “Both countries began the century by prohibiting the entry and incorporation of immigrants deemed undesirable because of their putative racial or ethno-national char-acteristics[…] Yet by the end of the century, both […] had developed into de facto multicultural societies ” (p. 2). This outcome came about as both nations responded to labour force needs and adjusted to the emergence of new international ethical norms against racism. Within this common trajectory, the research reported in this book documents wide differences in policy process and specific immigration policy and multicultural outcomes between the two nations. Becoming Multicultural addresses the politics of immigration for the two national cases by carrying out a long historical analysis of these politics, covering three periods: the first wave of globalization in the late 19th and early 20th centuries, the dismantling of colonialism in the mid-20th century, and the resurgence of immigra-tion and foreign worker flows in the late 20th century. This breadth of perspective for the comparative study of immigration and multiculturalism builds on the work initiated by various eminent scholars, such as Aristide

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.097

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.209
Teacher spread0.187 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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