A prototype of a receptive lexical test for a polysynthetic heritage language: The case of Inuttitut in Labrador
Bibliographic record
Abstract
This paper describes the process of designing, administering, and assessing a language-sensitive and culture-specific lexical test of Labrador Inuttitut (a dialect of Inuktitut, an Eskimo-Aleut language). This process presented numerous challenges, from choosing citation forms in a polysynthetic language to dealing with a lack of word frequency data. Twenty heritage receptive bilinguals (RBs) with very limited production skills in Inuttitut (their first language) and a comparison group of eight fluent bilinguals (FBs) participated in our study. Since the RBs lacked production skills in Inuttitut, the lexical test required participants to translate a carefully compiled list of Inuttitut nouns and verbs into English. The results revealed that RBs had good comprehension of basic vocabulary (85% accuracy), but differed significantly from FBs, mostly because the RBs had a number of partially accurate translations. The three lowest scoring RBs had the highest number of such translations as well as inaccurate translations based on phonological associations, as is common in emergent lexicons. This lexical test correlates with grammatical proficiency measures, pointing to its potential value as a quick placement and diagnostic test in revitalization programs for Inuttitut as well as other languages in a language loss situation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".