Elders’ Conversations: Perspectives on Leveraging Digital Technology in Language Revival
Bibliographic record
Abstract
In First Nations, Métis, and Inuit (FNMI) communities, Elders are highly regarded as intergenerational transmitters of ancestral language and Indigenous knowledge. Without language revival initiatives, ancestral languages in FNMI communities are at risk of extinction. Leveraging digital technologies while collaborating with Elders can support revival initiatives. Through semi-structured interviews and qualitative analysis, this study addresses how three Elders who use technology in their ancestral language teaching (1) describe the benefits, drawbacks, and preferences of technology; (2) reveal the accuracy with which cultural knowledge is imparted through technology; and (3) view the impact of technology on their role as traditional knowledge keepers and intergenerational language transmitters Findings suggest that while Elders acknowledge the benefits of leveraging digital tools in language revival initiatives, they are concerned about technology’s potential negative impacts on relationality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".