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Record W3122907133 · doi:10.1075/ml.19016.tsi

Does stress matter?

2020· article· en· W3122907133 on OpenAlexaff
Athanasios Tsiamas, Gonia Jarema, Eva Kehayia

Bibliographic record

VenueThe Mental Lexicon · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalJewish Rehabilitation HospitalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsStress (linguistics)Task (project management)FacilitationLexical decision taskPsychologyCognitive psychologyTest (biology)LinguisticsNatural language processingComputer scienceCognitionNeuroscience

Abstract

fetched live from OpenAlex

Abstract This study investigates the effect of stress change during compound processing in Modern Greek. Twenty-five native speakers were tested in a cross-modal lexical decision task and a naming task in order to test for performance differences across stress-change vs. non-stress-change compounds. No statistically significant difference was found for the lexical decision task. However, the naming task showed a significant effect of stress change in compound processing, with the production of non-stress-change compounds showing facilitation. These results indicate that stress change is reflected in compound processing in Greek and underscore the importance of considering the interplay between specific tasks and the computational role of linguistic features.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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