Bibliographic record
Abstract
This paper is focusing to analyze the multiculturalism developed countries through the seven components Migrant Integration Policy Index: MIPEX and comparative analysis based on the results of analysis. This paper adapt a method of concrete measures in seven areas consists of four sub indices to get a policy implication for Korea. The 2007(MIPEX II) and 2011(MIPEX III) were used to comparative analysis of the practices of France and Canada. France government highlighted the specific religious, cultural features of North African immigrants, and has imposed assimilation within French secular society. Canadian government, however, invented a legal frame work to guarantee the existence of the development as well as the security of its population and territory. This study derived the policy implications as follows. First, the multi-cultural society and integration is a long-term challenges to be pursued, to formate a social psychological mechanism on the multicultural expatriates in the respect of long-term vision. Second, the responsibility of the central government under independent supervision mechanism to manage the entire control to be effective. Third, study on the Muslim immigrants are promoted as part of a effective solution for locals and migrants, Fourth, the process and perception on the multo culturalism of the social consensus is important. Finally, multi-cultural policy and promotion strategy of repatriation program to maintain a sustainable immigrant system in Korea.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".