Indigenous Education in Taiwan: Policy Gaps, Community Voices, and Pathways Forward
Bibliographic record
Abstract
This study critically examines the state of Indigenous education in Taiwan through an interdisciplinary approach that integrates policy analysis, statistical evaluation, and localized case studies. Despite the implementation of progressive legislation, Indigenous students continue to encounter persistent disparities in both secondary and tertiary education. By drawing on national datasets and school-level examples, this paper uncovers systemic mismatches between mainstream educational practices and the linguistic, cultural, and communal realities of Indigenous populations. To contextualize Taiwan’s challenges, this study includes a comparative analysis with Indigenous education in Canada, highlighting both shared obstacles and divergent strategies. The findings indicate that, despite policy reforms and targeted programs in both nations, entrenched inequalities endure, rooted in colonial legacies, insufficient cultural integration, and a lack of community-driven educational initiatives. The article argues for a transformative shift in Taiwan’s education system: one that emphasizes the indigenization of curricula, the inclusion of Indigenous voices in educational policymaking, and greater investment in culturally responsive support mechanisms, particularly at the high school and university levels. In summary, meaningful improvement in Indigenous education requires moving from an assimilationist paradigm to one rooted in cultural respect and self-determination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".