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Tree Totems and the Tamarind People: Implications of Clan Plant Taboos in Central Flores

2009· article· en· W2093718350 on OpenAlexaff
Gregory Forth

Bibliographic record

VenueOceania · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Alberta
FundersLembaga Ilmu Pengetahuan IndonesiaBritish Academy
KeywordsClanToponymyGenealogyHuman settlementHistoryNarrativeTotemEthnologyPoliticsGeographyColonialismAnthropologySociologyArchaeologyLiteraturePolitical scienceLawArt

Abstract

fetched live from OpenAlex

ABSTRACT Among the Nage of eastern Indonesia, a sizeable minority of clans maintain totemic attitudes towards trees and other plants whose names they share. Tree totemism is mostly expressed in taboos on burning the wood and using the timber in construction. In addition, there is the idea that all Nage people should not burn wood of the Tamarind (Nage) tree. Comparative evidence and local historical narrative locate the source of phytonymic clan names in an earlier use of such names as toponyms and settlement names. Insofar as this is their origin, Nage plant totemism can thus be understood as a residue of a naming practice relating to places rather than to people, either human ancestors or groups. As the use of ‘Nage’ as the formal name of an inclusive ethnic, socio‐political, and territorial entity is relatively recent, and indeed largely a function of a colonial administration introduced barely a century ago, this case further demonstrates how taboos and totemic relations can develop rapidly in contexts of major social change.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.280
Teacher spread0.263 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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