sqwiilqwul’tul ‘words used when talking to one another’:
Bibliographic record
Abstract
This project explores the connections between the sounds of our land and the consonant sounds of our hul’q’umi’num’ language, a Coast Salish language spoken on Vancouver Island. These connections were made by listening to sounds found on the land in four areas within hul’q’umi’num’ speaking territory including the mountains, the forest, the river and the beach, and relating them to sounds in the language. In each area, speakers of hul’q’umi’num’ were recorded saying an individual consonant that reflected a sound of the land, as well as a corresponding word list. Some speakers also offered descriptions on how the sound is articulated. The sounds of the land, individual consonant sounds, word lists and the descriptions of articulation were woven together to create eight videos (one per sound), designed to support speakers in developing their pronunciation of the many consonant sounds of hul’q’umi’num’ that are not found in English. This land-based pronunciation resource is intended to develop the acquisition of hul’q’umi’num’ consonant sounds by inviting the land to participate as a teacher.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".