Being Indigenous in a Non-Indigenous Environment: Identity Politics of the Dogai Ainu and New Indigenous Policies of Japan
Bibliographic record
Abstract
The author discusses the identities and socioeconomic status of Indigenous people in a non-Indigenous environment, ways to recognize Indigenous belonging statistically, and ethnic policies in a Japanese context, specifically focusing on the Dogai Ainu; that is, the Ainu who left their original homeland of Hokkaido and live elsewhere in Japan. The Japanese Government's 2010 socioeconomic survey of the Dogai Ainu demonstrated a socioeconomic gap between the Dogai Ainu and the majority of the Japanese. This survey also revealed the difficulty of conducting surveys of the Dogai Ainu because, in a non-Indigenous environment, many of them tend to conceal their ethnicity for fear of discrimination and hesitate to participate in surveys. Indigenous peoples in Anglophone countries are increasingly challenging the definition of Indigeneity as imposed by outsiders, and self-identification is becoming an essential component of recognizing Indigenous belonging to reflect the reality and diversity of Indigenous identities. Some countries such as the USA and Canada have also begun using self-identification for enumeration in statistics. The case study of the Dogai Ainu, however, suggests that Indigenous belonging cannot always be recognized by self-identification and Indigenous policies may have to be implemented without comprehensive data.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".