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Record W4409416031 · doi:10.1075/ml.24028.moo

From ‘<i>jellyfish’</i> to ‘<i>poisson de gelée’</i>

2024· article· en· W4409416031 on OpenAlexaff
Mareike Moormann, Antje Lorenz, Lyndsey Nickels, Neville Hennessey, Britta Biedermann

Bibliographic record

VenueThe Mental Lexicon · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcMaster UniversityConcordia UniversityUniversity of Windsor
Fundersnot available
KeywordsJellyfishChemistryBiologyFishery

Abstract

fetched live from OpenAlex

Abstract This study investigated the representation of compound words in the mental lexicon by examining compound word production in bilingual speakers with aphasia. Eight bilingual speakers with aphasia named pictures of concepts with either compound or (non-compound) simple names in both of their languages. Error types were coded and analysed within and across languages with a particular focus on constituent and language mixing errors in compound words. Four participants showed significantly greater accuracy for simple than compound words, three in their dominant and/or more proficient language, and one in their non-dominant language. Constituent errors were observed for all eight bilingual participants during compound word naming, while language mixing errors were observed in six participants. The observed error patterns support co-activation of (compound) representations during word retrieval in bilingual speakers. Language mixing errors suggest that the bilingual lexicon stores a morphologically structured compound representation, an assumption that is consistent with a multiple-lemma representation of compounds. Further research is required to explore the extent to which constituent-specific access processes are at play in bilingual compound production.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.078
Threshold uncertainty score0.999

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)0.0000.001

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 head0.025
GPT teacher head0.307
Teacher spread0.282 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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