The Ainu and Indigenous politics in Japan: negotiating agency, institutional stability, and change
Bibliographic record
Abstract
Abstract In April 2019, the Japanese government officially legally recognized the Ainu as Indigenous people. Building on an institutionalist framework, the paper suggests that a phenomenon of institutional layering has taken place, resulting in tensions between the desire to preserve the legitimacy of old institutions and the pressure to develop more progressive policies. To explain this process, policy legacies, and institutional opportunities are relevant. First, the narrative that equality can be attained through assimilation, and the political construction of the “Ainu problem” as a regional one tied to Hokkaido pervade political imaginaries and institutions. Second, institutional opportunities have mediated the ways activists have sought to make their voices heard in the political arena. A focus on key historical segments illuminates the difficulty for activists to penetrate high-level political arenas while indicating the importance of agency, ties and interests in explaining major reforms and their limitations. The ambiguity that characterizes current policy framework points to the potential leverage that this policy configuration represents for the Ainu. At the same time, historical and institutional legacies that have shaped Indigenous politics continue to constrain, to a great extent, the possibilities for meaningful and transformative developments for the Ainu.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".