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Record W2578691053 · doi:10.4000/books.aaccademia.1775

Written word production and lexical self-organisation: evidence from English (pseudo)compounds

2016· book-chapter· en· W2578691053 on OpenAlex
Marcello Ferro, Franco Alberto Cardillo, Vito Pirrelli, Christina L. Gagné, Thomas L. Spalding

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

VenueAccademia University Press eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceLexical functional grammarLexical itemConnectionismLexical accessNatural language processingLexical choiceArtificial intelligenceLexical decision taskLatency (audio)LinguisticsPsychologyCognitionArtificial neural network

Abstract

fetched live from OpenAlex

Elevation in typing latency for the initial letter of the second constituent of an English compound, relative to the latency for the final letter of the first constituent of the same compound, provides evidence that implementation of a motor plan for written compound production involves smaller constituents, in both semantically transparent and semantically opaque compounds. We investigate here the implications of this evidence for algorithmic models of lexical organisation, to show that effects of differential perception of the internal structure of compounds and pseudo-compounds can also be simulated as peripheral stages of lexical access by a self-organising connectionist architecture, even in the absence of morphosemantic information. This complementary evidence supports a maximization-of-opportunity approach to lexical modelling, accounting for the integration of effects of pre-lexical and lexical access.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.952
Threshold uncertainty score0.862

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.240
Teacher spread0.210 · 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