MétaCan
Menu
Back to cohort
Record W2070495689 · doi:10.1075/ml.4.3.03mon

Lexical access of mass and count nouns

2009· article· en· W2070495689 on OpenAlex

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.

Bibliographic record

VenueThe Mental Lexicon · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalMcGill UniversityUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPluralNounComputer scienceLinguisticsFeature (linguistics)SentenceContext (archaeology)Natural language processingPriming (agriculture)Artificial intelligenceSentence processingHistory

Abstract

fetched live from OpenAlex

Two psycholinguistic experiments were carried out in Italian to test the role played by the feature that distinguishes mass nouns from count nouns, as well as by the feature that distinguishes singular nouns from plural nouns. The first experiment, a simple lexical decision task, revealed a sensitivity of the lexical access system to the processing of the features Mass and Plural as shown by longer reaction times. In particular, nouns in the plural yielded longer reaction times than in the singular except when the plural form was irregular. Furthermore, the feature Mass also affected processing, yielding longer reaction times. In the second experiment, a sentence priming task, both the Plural and the Mass effects did not surface when a grammatical sentence fragment was the prime. These data show a direct correlation between the linguistic ‘complexity’ of plural/mass nouns and processing time. They also suggest that this complexity does not affect normal fluent spoken language where words are embedded in a semantic and syntactic context.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.183

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 head0.041
GPT teacher head0.328
Teacher spread0.287 · 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