yaʕtmín cqwəlqwilt nixw, uł nixw, ul nixw, I need to speak more, and more, and more: Okanagan-Colville (Interior Salish) Indigenous second-language learners share our filmed narratives
Bibliographic record
Abstract
way’, iskwíst, my name is, Sʔímlaʔxw, and I am from Penticton BC, Canada. kn sqilxw. I am a Syilx (Okanagan, Interior Salish) adult language learner. My cohort and I are midway in our language transformation to become proficient speakers. Our names are Prasát, Sʔímlaʔxw, C’ər̓tups, X̌wnámx̌wnam, Staʔqwálqs, and our Elder, Sʕamtíc’aʔ. We created an adult immersion house, deep in Syilx territory, and lived and studied together for five months. We combined intensive curricular study, cutting-edge second-language acquisition techniques, filmed assessments, and immersion with our Elder. We emerged transformed—we are n’łəqwcin, clear speakers, speaking at an intermediate level. There has been very little written about assessment of Indigenous language teaching methods or Indigenous language speaking ability, and much less written about filmed learning and assessment. Three films were created in our language, nqilxwcn, and placed on YouTube. The films give primacy to our personal narratives, document and share our transformation, speaking abilities, grassroots language activism and learning methods. This paper describes the films, my cohort’s transformation, assesses our speaking ability, describes Paul Creek Language Association curriculum, and represents a contribution to Indigenous language teaching methods, assessment and nqilxwcn revitalization. iskwíst Sʔímlaʔxw, kn t̓l snpintktn. kn sqilxw uł kn səcmipnwíłn nqilxʷcn. axáʔ inq̓əy̓mín iscm̓aʔm̓áy. kwu kcilcəl̓kst kwu capsíw̓s, iʔ sqəlxwskwskwístət Prasát, Sʔímlaʔxw, C̓ər̓tups, X̌wnámx̌wnam, Staʔqwálqs, naʔł iʔ ƛ̓x̌aptət, Sʕamtíc̓aʔ. kwu kwliwt l̓ nqilxwcn iʔ citxwtət cilkst iʔ x̌yałnəx̌w uł isck̓wúl̓ kaʔłís iʔ tə syaʔyáʔx̌aʔ. iʔ l̓ syaʔyáʔx̌aʔtət, caʔkʷ mi wikntp iʔ scm̓am̓áy̓aʔtət iʔ kłyankxó nqilxʷcn iʔ sc̓ʕaʕ̓ác̓s, kwu cnqilxʷcnm, kwu səck̓waʔk̓wúl̓m nqilxʷcn, uł kwu x̌əstwilx iʔ scqwaʔqwʔáltət. xəc̓xac̓t iʔ sck̓wul̓tət, naxəmł ksxan iʔ tl̓ silíʔtət iʔ l̓ kiʔláwnaʔ iʔ sn̓ilíʔtns kwu ctixwlm. ʕapnáʔ kwu capsíw̓s uł kwu n̓łəqwcin. wtntím iʔ syaʔyáʔx̌aʔtət l̓ YouTube uł iʔ scx̌minktət caʔkw ksʕaysnwím iʔ scsm̓am̓áy̓tət, kłyankxó iʔ sck̓wul̓səlx, uł caʔkw cʔkin iʔ ł sk̓ʷaʔk̓ʷúlm iʔ nqəlqílxʷcn iʔ kscm̓am̓áy̓aʔx, uł caʔkw mi łxʷl̓al aʔ nqəlqilxwcntət.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".