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Record W4372319438 · doi:10.1080/13537113.2023.2205686

Ideational Models of Immigrant Integration in Japan: A Multi-Scalar Approach to the Dynamics of Policy Frames

2023· article· en· W4372319438 on OpenAlexaff
Eléonore Komai

Bibliographic record

VenueNationalism and Ethnic Politics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsFraming (construction)MulticulturalismCorporate governanceImmigrationPoliticsPolitical scienceSociologyPolitical economyCitizenshipEconomyGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

This article explores migrant integration policy frames in Japan based on a multi-scalar research design. The development of migrant integration frames mirrors a process where the local scale has contributed to the development of a national policy based on the concept of “multicultural coexistence.” Under the impulsion of immigration reforms, the central government has consolidated the national framework and strengthened its involvement in the governance of migrant integration turning to a more economic framing of migrants. While the cases of Aichi prefecture and Nagoya and Toyohashi cities (located in Aichi prefecture) reflect a gradual convergence of frames with the national level, policies in Kyoto prefecture and Kyoto city do not echo such shifts. Surprisingly, Kyotango city located in Kyoto prefecture has drawn on national level policies turning to a more economic framing of migrants. A focus on “relationality” and stakeholders in policy formulation and relationships between different scales of governance suggests that the assemblage of local political actors bringing their priorities to the discussion table are important shaping forces of local policy frame development. At the same time, exchanges in horizontal and vertical networks exhibit the vitality of the circulation of ideas, even in the absence of formal coordination mechanisms.

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 score0.747
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.078
GPT teacher head0.377
Teacher spread0.299 · 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
Published2023
Admission routes1
Has abstractyes

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