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Record W2606521171 · doi:10.1111/tops.12268

Multiunit Sequences in First Language Acquisition

2017· article· en· W2606521171 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

VenueTopics in Cognitive Science · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsInternational Development Research Centre
FundersEconomic and Social Research Council
KeywordsRule-based machine translationConstruct (python library)Computer scienceAbstractionWord orderLinguisticsMeaning (existential)Word (group theory)Natural language processingArtificial intelligenceCognitive sciencePsychologyProgramming language

Abstract

fetched live from OpenAlex

Theoretical and empirical reasons suggest that children build their language not only out of individual words but also out of multiunit strings. These are the basis for the development of schemas containing slots. The slots are putative categories that build in abstraction while the schemas eventually connect to other schemas in terms of both meaning and form. Evidence comes from the nature of the input, the ways in which children construct novel utterances, the systematic errors that children make, and the computational modeling of children's grammars. However, much of this research is on English, which is unusual in its rigid word order and impoverished inflectional morphology. We summarize these results and explore their implications for languages with more flexible word order and/or much richer inflectional morphology.

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 score0.146
Threshold uncertainty score0.992

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.397
Teacher spread0.348 · 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