Investigating the concept of success in the Bachelor of Education in Indigenous Language Revitalization Program in the WSÁNEĆ Community and at the University of Victoria
Bibliographic record
Abstract
Indigenous languages account for many of the languages worldwide and, importantly, they help to maintain Indigenous knowledge and epistemologies. However, many of these languages will not be handed down to future generations. Given this, language revitalization programs are designed to preserve and revitalize endangered languages as well as equip language revitalizers with the capabilities to teach those languages and explore methods to marry non-Indigenous and Indigenous knowledges. Currently, language revitalization programs are investigated for their success in providing for the linguistic and pedagogical needs of the people involved in the programs. In Canada, approaches to maintain and preserve Indigenous languages are developed via Indigenous language revitalization programs through consultation with Indigenous knowledge keepers about their languages and pedagogies. The Bachelor of Education in Indigenous Language Revitalization (BEDILR) program is offered at the University of Victoria and the W̱SÁNEĆ community in British Columbia, where it focuses on the W̱SÁNEĆ epistemology and revitalizes the SENĆOŦEN language. This doctoral research examined the concept of success in the BEDILR program for the W̱SÁNEĆ people. In doing so, the researcher conducted participatory action research (PAR) as well as semi-structured interviews to collect data from five academic and community participants in the W̱SÁNEĆ community and the University of Victoria. Next, the researcher followed Owen’s (1984) thematic network to analyze the data for codes and themes. Findings suggest there are four themes of Indigenous person, approach of the BEDILR program, Indigenous pedagogy, and assessment and seven sub-themes of method, approach dynamics, Indigenous pedagogy of language of instruction, content of the program, and objectives of the program, assessor from inside community and external assessor for the W̱SÁNEĆ community participants. Also, there were four themes of Indigenous person, approach, pedagogy, and assessment and six sub-themes of method, approach dynamics, content of the program, objectives of the program, assessor, and what to assess for the academic participant. Specifically, participants in this study believed the BEDILR is a successful language revitalization program because it incorporates Indigenous pedagogy and follows an approach and assessment method based on the W̱SÁNEĆ worldviews, knowledge, epistemology, and the SENĆOŦEN language.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".