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Patterns of National Integration in Response to Growing Immigration

2015· article· en· W3135042638 on OpenAlexaboutno aff
Ksenia Zaika

Bibliographic record

VenueWorld Economy and International Relations · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Economic Relations
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPolitical scienceIdeologyImmigration policyPolitical economyPoliticsDevelopment economicsInstitutionalisationSociologyLawEconomics

Abstract

fetched live from OpenAlex

The paper summarizes immigration policy models in Germany, France, the UK, the USA and Canada in response to the growing immigration flows since the second half of the XXth century. By contrast with “traditional” nation states of Western Europe, the USA and Canada have developed on the basis of the settler colonies having melted immigrants of various ethnic and cultural origins. The USA and Canada have been prioritizing immigration as a factor of their national development. Although public culture in these immigration states has been developing on some specific cultural patterns, American and Canadian societies have not generated the concepts and perceptions of an “ethno-cultural” core of the nation as such. One of the reasons for current integration issues in the West is the struggle of immigrants for their collective cultural rights in host societies. Differences in political culture between Western European states and immigration states (the USA and Canada in this case) determine the gap in the institutionalization of collective rights for immigrants and, correspondingly, specific character of integration issues. Transition to the pluralistic model of national integration in the USA and Canada is determined by the following factors. First, due to mass immigration, there emerged liberalization of immigration policies. Secondly, in the post-war period, political cultures in liberal democracies witnessed a pronounced republican tendency, due to the ideological influence generated in times of The African-American Civil Rights Movement.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.314
Teacher spread0.276 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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