Real People, Real Dogs, and Pigs for the Ancestors: The Moral Universe of “Domestication” in Indigenous Taiwan
Bibliographic record
Abstract
ABSTRACT Humans and other animals often engage in multispecies relations that go beyond classical definitions of “domestication,” not least because there are political dimensions to those relations. External interference with human–animal relationships has notably been part of indigenous experiences of colonialism and postcolonialism. I examine here changes in the triangular relationship between humans, dogs, and pigs among the indigenous Seejiq Truku of Taiwan. Dogs, as hunting companions, are traditionally associated with men's work; pigs, used in ancestral sacrifices, are aligned with women's work. Pigs are mediators with the spirit world, as ancestor spirits respond to regular pig sacrifices by providing prey to hunters. Dogs are important as hunting companions that make it possible to catch boars and other animals. These human–animal relations have undergone change because of the integration of the Seejiq into new markets, the state, and legal regulations about both hunting and the keeping of animals. Human–animal relations also articulate with dynamics of gender and class in a changing political economy. The Seejiq frame their intrahuman and interspecies relations in terms of Gaya, their sacred ancestral law. By affirming the value of their particular type of multispecies community, the Seejiq demonstrate resilience and a strong defense of sovereignty. [ multispecies ethnography, indigenous peoples, Taiwan, human–animal relations, postcolonialism ] PUSU KARI QRQUR Pnegluban seejiq ni kana samat o saw bi tkrakaw sun imi “nguciq,” aji asaw quri pnegluban quri kmlawa ka nii. Qarat paah ngangut saw pnegluban seejiq ni samat nii o kibi saw niqan cih rutut na quri saw yahan kmnlawa seejiq ni kbukuy yahan kmnlawa seejiq. Qtaun mu hini o, tru pnegluban quri seejiq, huling, ni babuy mniq alang Truku Teywan hini. Huling o, ida tuhuy snaw musa maduk tkjiyax; babuy do o, duhuy kkuyuh musa bi thmuku rudan sbiyaw. Babuy o, mniq kska seejiq ni utux, kibi dmka saw muway samat seejiq maduk ka utux rudan. Huling ka pusu balay, aji wana tuhuy seejiq nanak, asi ka smtama dhyaan musa maduk bowyak ni kana samat. Pnegluban seejiq ni kana samat ni o, wada kmpriyux da; yasa wada tmay burah alang ni kndsan ka seejiq ni kmbryux kana ka uda saw maduk uri da. Pnegluban seejiq ni kana samat o kibi saw rmngaw quri kmbriyux kkuyuh ni snaw aji uri o sblaiq ni qrinut babaw dxgan sayang. Seejiq o, rmlung saw quri pnegluban kska seejiq ni aji uri o pnegluban isil siida do, asi ka rmlung gaya rudan dha nanak. Saw ni qmita pusu malu kska alang ana manu o, pqtayun dha ka lbay dha ni hlakkun dha bi ka saw quri brax dha. [pelealay lala knlgan, seejiq tnpusu, Teywan, plutut seejiq ni samat, bukuy kmlawa]
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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 | 0.001 |
| 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 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".