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Record W2798117238 · doi:10.5334/gjgl.359

Quantity judgment studies in Yudja (Tupi): Acquisition and interpretation of nouns

2018· article· en· W2798117238 on OpenAlexaff
Suzi Lima

Bibliographic record

VenueGlossa a journal of general linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNounInterpretation (philosophy)Object (grammar)LinguisticsProper nounPsychologyMeaning (existential)Philosophy

Abstract

fetched live from OpenAlex

This paper explores the acquisition path and interpretation of substance and object nouns in Yudja, a Brazilian indigenous language. Based on quantity judgment tasks (Barner & Snedeker 2005), we show that children accept both cardinal and non-cardinal interpretations for all nouns (object and substance denoting nouns), while adults strongly favor a cardinal interpretation for all nouns, including substance nouns. We will use the results from these studies to support three theoretical claims from the literature. First, that the pattern observed for adults corroborates previous analyses of Yudja according to which maximal self-connected concrete portions of a kind can be considered as atoms and can be counted (Lima 2014). Second, that counting does not require natural atomicity (cf. Rothstein 2010). Third, that the definition of atoms for counting is the result of lexical, syntactic and pragmatic factors and does not depend solely on the lexical meaning of a noun (cf. Srinivasan & Barner 2016).

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.313
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 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

Citations43
Published2018
Admission routes1
Has abstractyes

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