Social markers of acceptance in Japan: examining acceptance criteria for immigrants of different ethnocultural heritages / <i>Marcadores sociales de aceptación en Japón: examen de los criterios de aceptación de inmigrantes de diferentes herencias etnoculturales</i>
Bibliographic record
Abstract
This study utilized social markers of acceptance (SMA) to understand whether and how Japanese host national inclusiveness changes according to immigrant place of origin. SMA are socially constructed benchmarks (e.g., linguistic proficiency or genealogy) that receiving nationals use in deciding whether to view immigrants as national ingroup members. Japanese nationals ( N = 1,309) participated in an online survey to identify how SMA importance varied with perceptions of immigrant threat, contribution, status and intergroup permeability towards immigrants from China, South America and Western countries. Respondents emphasized ethnic and civic SMA more, becoming less inclusive across all three groups if immigrants were viewed as posing high levels of threat. Differences in marker emphasis towards the immigrant groups were found for perceived immigrant contributions and intergroup permeability. The latter finding underscores that Japanese people may need less permeable intragroup boundaries and a sense of psychological distance before becoming accepting of some immigrants, while more permeable boundaries and a sense of similarity may benefit others in being accepted. Chinese people were seen as the most threatening, Westerners as highest in status and South Americans (who primarily do unpopular blue-collar jobs) as highest in contributions yet lowest in status — suggesting that Japanese view immigrant contributions primarily in terms of doing blue-collar work that Japanese eschew. Overall, the findings did not demonstrate unambiguous double standards in acceptance criteria but rather the shifting role of SMA in constructing social boundaries depending upon the immigrant group being considered, with each boundary condition reflecting different obstacles and enablers for immigrants to belong. Such patterns differed from Western countries, as immigrants to Japan were not necessarily accepted from wealthy nations or the same ethnic group as the receiving majority. Attitudes towards immigrants in Japan were concluded to be both universal and group-specific.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".