Calculating mean length of utterance for eastern Canadian Inuktitut
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".