Ideational Models of Immigrant Integration in Japan: A Multi-Scalar Approach to the Dynamics of Policy Frames
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".