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Record W2742990131 · doi:10.14237/ebl.8.1.2017.900

‘Fish’ and ‘Non-Fish’ in Lio and Nage: Folk-Intermediates and Folk-Generics in the Fish Classification of Two Eastern Indonesian Peoples

2017· article· en· W2742990131 on OpenAlexaff
Gregory Forth

Bibliographic record

VenueEthnobiology Letters · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTaxonFish <Actinopterygii>IndonesianCategorizationHistoryFreshwater fishGenealogyZoologyBiologyLiteratureEcologyLinguisticsPhilosophyArtFishery

Abstract

fetched live from OpenAlex

Based on recent field research on Flores Island, this paper describes the classification of fish found among the Lio people. Formally, Lio fish taxonomy closely resembles that of the Nage of central Flores, discussed in a previous paper (Forth 2012), but differs insofar as several kinds of freshwater fish, all members of the Gobioidei, are subsumed in a named folk-intermediate taxon labeled mbo. Most attention is given to Lio names for folk-generics included in this intermediate. These correspond to the same species and genera included in a Nage folk-intermediate which, however, is unnamed. Moreover, Lio names for the component generics are clearly motivated by the same morphological and behavioral features as are reflected in Nage names for the same generics, yet the Lio names themselves are lexically quite different. These simultaneous classificatory similarities and nomenclatural differences are discussed with reference to the parts played by a common cultural heritage and natural discontinuity in the categorization of fish among these two ethno-linguistically related groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.291
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2017
Admission routes1
Has abstractyes

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