Calculating mean length of utterance for eastern Canadian Inuktitut
Why this work is in the frame
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Bibliographic record
Abstract
Although virtually all Inuit children in eastern Arctic Canada learn Inuktitut as their native language, there is a critical lack of tools to assess their level of language ability. This article investigates how mean length of utterance (MLU), a widely-used assessment measure in English and other languages, can be best applied in Inuktitut. The authors seek a measure that is suitable for the structural characteristics of Inuktitut as well as the practical realities of language assessment in the Inuit context. They compare five measures of mean length of utterance/word as well as five measures of longest utterance/word using three sets of data: spontaneous speech from eight children aged 1;8–3;6, frog story narratives from 12 older children and 6 adults, and spontaneous speech from one 5-year-old with specific language impairment and an age-matched peer. The authors conclude that mean length of word in syllables is the measure that provides the best balance of reliably assessing language level while also suiting Inuktitut structure and being relatively easy to calculate.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it