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Record W7071397468

Sensing Others: Voicing Batek Ethical Lives at the Edge of a Malaysian Rainforest

2023· article· en· W7071397468 on OpenAlexaboutno aff

Bibliographic record

VenueSOAS Research Online (SOAS University of London) · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Research
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPrecarityEthnographyRainforestNegotiationMetisEveryday life
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0170.018
Scholarly communication0.0070.009
Open science0.0010.008
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.116
GPT teacher head0.333
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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