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Record W2095922715 · doi:10.1080/01690965.2013.813562

Stress consistency and stress regularity effects in Russian

2013· article· en· W2095922715 on OpenAlex
Olessia Jouravlev, Stephen J. Lupker

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLanguage Cognition and Neuroscience · 2013
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStress (linguistics)Consistency (knowledge bases)SyllableNounLinguisticsLexical decision taskNatural language processingPsychologyWord (group theory)SpellingComputer scienceArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

This paper presents findings from the analysis of a Russian word corpus and two studies assessing the effects of stress consistency and stress regularity on performance in naming and lexical decision tasks. An examination of the impact of stress in Russian is particularly interesting because, although there is no regular stress pattern overall, first-syllable stress is regular for adjectives. The results demonstrated a processing advantage for regularly stressed adjectives in both tasks. For nouns and verbs, which have no clear regular stress pattern, no differences in the processing of initial- vs. final-stressed words were observed. Further, an advantage in the processing of words with consistent vs. inconsistent spelling-to-stress mappings was detected for all words in naming, but only for irregularly stressed adjectives in lexical decision. These findings provide evidence that readers are sensitive to both stress consistency and stress regularity even when regularity exists only for words of a single grammatical category.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.555
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.286
Teacher spread0.273 · 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