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Record W1863369831 · doi:10.1177/117718011300900406

The Preservation of Canadian Indigenous Language and Culture through Educational Technology

2013· article· en· W1863369831 on OpenAlexaffabout
Andrew Kitchenham

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsIndigenousIndigenous languageExperiential learningExtant taxonThe InternetEducational technologyTraditional knowledgePsychologyMathematics educationPedagogyComputer scienceSociologyMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

This study was a preliminary investigation into the preservation of Indigenous language and culture through educational technology. Using the research methods of an online questionnaire, on-site visits, semi-structured interviews and reflective journals, I examined current methods adopted by Aboriginal Language and Culture (ALC) teachers in British Columbia. This article provides the summary of the online questionnaire. The results indicate that teachers are using a mix of outdated non-technological second-language methods such as flashcards and worksheets with more-recent methods such as Elders and experiential learning. The results also indicate that there is a mismatch between what teachers report as the methods they use and the effectiveness of those methods. The teachers are using technological tools such as interactive whiteboards, digital video cameras and the Internet, but they do not appear to be accessing online resources that are either targeted for Indigenous language use or could be easily adapted for language preservation. The extant literature on Indigenous learning styles and on the use of technology in Indigenous language and culture classrooms supports my findings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.034
GPT teacher head0.411
Teacher spread0.377 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2013
Admission routes2
Has abstractyes

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