Bibliographic record
Abstract
This research explored the linguistic rhythm of Hul’q’umi’num’, a dialect of the Coast Salish Hul’q’umi’num’-Halq'eméylem-həəmiə language, and initiated phonetic documentation of it. Rhythm was analyzed from an audio file of an Elder telling a story. The story was segmented and phonetically transcribed using acoustic analysis software (Praat), and rhythm was measured based on the segmentation. Rhythm metrics demonstrated that consonantal intervals of Hul’q’umi’num’ patterned like no other documented language (according to ΔC and VarcoC). In terms of vocalic intervals (%V, ΔV, and VarcoV), Hul’q’umi’num’ patterned in the same rhythmic category as English (“stress-timed”). Interestingly, several differences emerged between the segmentation-based phonetic transcription and the transcription provided by language experts, such as consonant cluster elisions, loss of glottal stops, and vowel alternations. Further investigation of their systematicity and effects on Hul’q’umi’num’ rhythm as a whole is needed to understand what components of the language give it its unique rhythm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".