The use of verbal inflections in Inuktitut child and child-directed speech
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".