Syilx Language House: How and Why We Are Delivering 2,000 Decolonizing Hours in Nsyilxcn
Bibliographic record
Abstract
The Syilx Language House has completed two years of a four-year, 2,000-hour program to create new adult Nsyilxcn speakers, based on Syilx communities’ specific priorities. Our critically endangered status requires radically decolonizing teaching techniques. Nsyilxcn (Okanagan) teachers are learners, trained to deliver sequenced curriculum in full immersion using cutting-edge teaching techniques. Teachers employ strategies that prioritize effective immersion, frequent assessment, and a high level of classroom safety. This article shares our story, applied teaching methods, student testimonials, community feedback, and our 2020 Plan. After completing our second year, students have completed 900 hours of intensive immersion. Students state that our teaching methods are the fastest, most effective language learning they have ever experienced. After four years, students will emerge as mid- to high-intermediate speakers, capable of bringing language into homes, teaching new cohorts of adults, and creating immersion workplaces.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".