Between Legal Indigeneity and Indigenous Sovereignty in Taiwan: Insights From Critical Race Theory
Bibliographic record
Abstract
Taiwan, home to over 580,000 Indigenous people in 16 state‐recognized groups, is one of three Asian countries to recognize the existence of Indigenous peoples in its jurisdiction. Taiwan’s Indigenous peoples remember their pre‐colonial lives as autonomous nations living according to their own laws and political institutions, asserting that they have never ceded territory or sovereignty to any state. As Taiwan democratized, the state dealt with resurgent Indigenous demands for political autonomy through legal indigeneity, including inclusion in the Constitution since 1997 and subsequent legislation. Yet, in an examination of two court rulings, we find that liberal indigeneity protects individuals, while consistently undermining Indigenous sovereignty. In 2021, the Constitutional Court upheld restrictive laws against hunting, seeking to balance wildlife conservation and cultural rights for Indigenous hunters, but ignoring Indigenous demands to create autonomous hunting regimes. In 2022, the Constitutional Court struck down part of the Indigenous Status Act, which stipulated that any child with one Indigenous parent and one Han Taiwanese parent must use an Indigenous name to obtain Indigenous status and benefit from anti‐discrimination measures. Both rulings deepen state control over Indigenous lives while denying Indigenous peoples the sovereign power to regulate these issues according to their own laws. Critical race theory (CRT) is useful in understanding how legislation designed with good intentions to promote anti‐discrimination can undermine Indigenous sovereignty. Simultaneously, studies of Indigenous resurgence highlight an often‐neglected dimension of CRT—the importance of affirming the nation in the face of systemic racism.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".