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Record W7006765936

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

2014· article· en· W7006765936 on OpenAlexaboutno aff

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousIndigenous languageGrassrootsNarrativeLanguage revitalizationFirst languageLanguage acquisitionStorytelling
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.227
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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