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Record W4379987887 · doi:10.1558/jmbs.23491

The use of verbal inflections in Inuktitut child and child-directed speech

2023· article· en· W4379987887 on OpenAlexaboutno aff
Hannah Lee, Olga Alice Johnson, Shanley Allen

Bibliographic record

VenueJournal of Monolingual and Bilingual Speech · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAgglutinative languagePsychologyLinguisticsDevelopmental psychologyLanguage developmentMorpheme

Abstract

fetched live from OpenAlex

Inuktitut is a polysynthetic agglutinative language of the Inuit-Yupik-Unangan language family, with nearly 900 verbal inflections. Despite the complexity of its inflectional system, children acquiring Inuktitut as their native language start using inflections relatively early (Crago & Allen, 2001; Swift & Allen, 2002). One hypothesis is that caregivers simplify their child-directed speech (CDS) in a way that helps the children to break into the system. To date, relatively little research has focused on the use of inflections in CDS. The current study uses the data from eight Inuktitut-speaking children aged 1-4 years and their mothers to investigate whether and how the use of verbal inflections (VIs) in CDS changes as the children advance linguistically, and whether the children's use of VI corresponds with the input they receive. We found a significant increase in the number of different VIs and the total number of VIs in the mothers’ CDS as their children went from Stage 1 to Stage 6 of linguistic development. Children's use of VIs follows the general patterns of VI acquisition cross-linguistically. Further, as children progressed linguistically, they seemed to rely less on the input from their mothers, since they increasingly used VIs not previously found in their mothers’ CDS (from 16% in Stage 2 to 75% in Stage 6). These results correspond with other studies’ findings of CDS simplification and extend our understanding of how inflectional morphology is adapted in CDS.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.307
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2023
Admission routes1
Has abstractyes

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