Land is Life: Settler Colonial Governance of National Parks and Hunting in Taiwan
Bibliographic record
Abstract
This thesis situates Taiwan as a settler colonial state by examining the discourse around the governance of national parks and the criminalization of Indigenous hunting. Placed in the context of historical patterns of land dispossession and cultural genocide, these two issues represent the ongoing process of settler colonialism and the reproduction of settler colonial relations through environmentalism. I focus on the narratives around three case studies: the controversial and ultimately unsuccessful campaign for the Maqaw National Park, the Tumpu Daingaz buluo’s struggle with the Yushan National Park, and the Tama Talum Indigenous hunting constitutional reinterpretation case. I argue that settler colonial framings of Indigenous/environmental issues enable the continued enactment of colonial relations and policies. Settler narratives and environmentalism perpetuate settler colonialism through what Métis scholar Max Liboiron explains as the assumption of access to Indigenous land, cultures, and knowledge. These cases are often framed as a progressive and benevolent government inclusion of Indigenous cultures and ecological knowledge. However, a settler colonial lens of analysis demonstrates that these moves of settler inclusivity serve to preserve settler legitimacy and futures in Taiwan while deeper contentions over Indigenous sovereignty remain unresolved. Indigenous voices within these stories reveal a throughline of ongoing resistance and resurgence, offering alternative understandings that center Indigenous land and life. While settler narratives portray and encourage limiting frameworks that prioritize settler interests, Indigenous narratives and activism expand the ways for Indigenous self-determination, futures, and land relations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".