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Record W2560390597 · doi:10.4148/1944-3676.1113

Bare Nouns in Brazilian Portuguese: An experimental study on grinding

2016· article· en· W2560390597 on OpenAlexaff
Kayron Beviláqua, Suzi Lima, Roberta Pires de Oliveira

Bibliographic record

VenueThe Baltic International Yearbook of Cognition Logic and Communication · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of Toronto
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsInterpretation (philosophy)NounPortugueseBrazilian PortugueseLinguisticsGrindingProper nounVolume (thermodynamics)PhilosophyMathematicsPhysicsEngineeringThermodynamicsMechanical engineering

Abstract

fetched live from OpenAlex

Much literature has explored the interpretation of the bare singular (BS) in Brazilian Portuguese. Pires de Oliveira and Rothstein (2011) claim that BS nouns are mass because they denote kinds and argue that this explains why only the BS in Brazilian Portuguese can have a non-cardinal interpretation. In this paper, based on an experimental task with Brazilian Portuguese adult speakers, we explore one of their predictions, namely that the ‘volume interpretation’ of the BS cannot be explained as a case of Grinding. Our results show that Grinding and Volume readings of a BS noun are not equivalent (in favor of their hypothesis). We also show that a volume interpretation of a noun is never preferred when a cardinal interpretation is available, but that this can be explained by other lexical and pragmatic factors. We conclude by suggesting that Rothstein’s (in press) distinction between counting and measuring accounts for the fact that non-cardinal readings are not grinding.

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)0.004
metaresearch head score (Gemma)0.015
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 score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.056
GPT teacher head0.304
Teacher spread0.249 · 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

Citations64
Published2016
Admission routes1
Has abstractyes

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