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Record W4283123988 · doi:10.16995/jpl.8188

Lexical access in Portuguese stress

2022· article· en· W4283123988 on OpenAlexaff
Guilherme D. Garcia, Natália Brambatti Guzzo

Bibliographic record

VenueJournal of Portuguese Linguistics · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPhonotacticsCategorical variableLexiconEquivalence (formal languages)Lexical decision taskStress (linguistics)LinguisticsMathematicsBrazilian PortugueseNatural language processingComputer scienceStatisticsPsychologyPortuguesePhonologyCognition

Abstract

fetched live from OpenAlex

Categorical approaches to lexical stress typically assume that words have either regular or irregular stress, and imply that only the latter needs to be stored in the lexicon, while the former can be derived by rule. In this paper, we compare these two groups of words in a lexical decision task in Portuguese to examine whether the dichotomy in question affects lexical retrieval latencies in native speakers, which could indirectly reveal different processing patterns. Our results show no statistically credible effect of stress regularity on reaction times, even when lexical frequency, neighborhood density, and phonotactic probability are taken into consideration. The lack of an effect is consistent with a probabilistic approach to stress, not with a categorical (traditional) approach where syllables are either light or heavy and stress is either regular or irregular. We show that the posterior distribution of credible effect sizes of regularity is almost entirely within the region of practical equivalence, which provides strong evidence that no effect of regularity exists in the lexical decision data modelled. Frequency and phonotactic probability, in contrast, showed statistically credible effects given the experimental data modelled, which is consistent with the literature.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.343
Teacher spread0.289 · 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.

Study designBench or experimental
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

Citations6
Published2022
Admission routes1
Has abstractyes

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