Sensing Others: Voicing Batek Ethical Lives at the Edge of a Malaysian Rainforest
Bibliographic record
Abstract
Sensing Others explores the lives of Indigenous Batek people in Peninsular Malaysia amid the strange and the new in the borderland between protected national park and oil palm plantation. As their ancestral forests disappear around them, Batek people nevertheless attempt to live well among the strange Others they now encounter: out-of-place animals and plants, traders, tourists, poachers, and forest guards. How Batek people voice their experiences of the good and the strange in relation to these Others challenges essentialized notions of cultural and species difference and the separateness of ethical worlds.Drawing on meticulous, long-term ethnographic research with Batek people, Alice Rudge argues that as people seek to make habitable a constantly changing landscape, what counts as Otherness is always under negotiation. Anthropology’s traditional dictum to “make the strange familiar, and the familiar strange” creates a binary between the familiar and the Other, often encapsulating Indigenous lives as the archetypal Other to the “modern” worldview. Yet living well amid precarity involves constantly negotiating Otherness’s ambivalences, as people, plants, animals, and places can all become familiar, strange, or both. Sensing Others reveals that when looking from the boundary, what counts as Otherness is impossible to pin down.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".