MétaCan
Menu
Back to cohort
Record W2625926488 · doi:10.3765/plsa.v2i0.4077

Overriding default interpretations through prosody: Depictive predicates in Brazilian Portuguese

2017· article· en· W2625926488 on OpenAlexafffund
Natália Brambatti Guzzo, Heather Goad

Bibliographic record

VenueProceedings of the Linguistic Society of America · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInterpretation (philosophy)ProsodySentencePsychologyLinguisticsTask (project management)Context (archaeology)Object (grammar)JudgementIntonation (linguistics)

Abstract

fetched live from OpenAlex

In Brazilian Portuguese, depictive predicates can have ambiguous readings: the attribute can either refer to the subject (high attachment; HA) or the object (low attachment; LA) of the sentence. Previous studies have found that LA is the default interpretation for ambiguous depictive predicates (e.g., Magalhães & Maia 2006, Fonseca & Magalhães 2007), and that speakers use different acoustic cues to signal HA. However, these studies found several mismatches between speakers’ intended intonation and listeners’ interpretations. We conducted a judgement task and a production task to determine which acoustic cues are used by native speakers to arrive at HA interpretation. The results for the judgement task indicate that HA interpretation is favored by pause before attribute (which can be combined with another cue in the attribute). In the production task, speakers can also signal HA by putting a pause before the attribute (which can be combined with another cue in the object). However, some of the participants did not use any acoustic cue to signal HA, which suggests that some speakers arrive at a HA interpretation only through context, not prosody.

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)0.018
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.023
GPT teacher head0.271
Teacher spread0.248 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Linguistic Society of AmericaSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207