Hul’q’umi’num’ listening quizzes
Bibliographic record
Abstract
Abstract In this paper, we discuss a set of 60 listening quizzes, created to support adult learners of Hul’q’umi’num’ (Coast Salish) in fine-tuning their listening and speaking skills. Hul’q’umi’num’ has a rich consonant inventory, including many sounds not found in learners’ L1 (English). The goal of the quizzes was twofold: provide learners with opportunities to practice hearing these sounds and, at the same time, inform us about the features of Hul’q’umi’num’ L2 speech perception. Findings showed which sounds were particularly easy or challenging, laying the foundation for creating more targeted resources to better aid sound acquisition among Hul’q’umi’num’ learners. Evidence of improvement in perceptual ability after taking the quizzes was also found. This work contributes to diversifying scientific approaches to second language acquisition by showing how speech perception research and pedagogy can be combined in an Indigenous language revitalization context.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".