Connection, collaboration and community : reflections on the use of videoconferencing in Kaska language documentation, revitalization and education
Bibliographic record
Abstract
This thesis addresses how stakeholders of Kaska, a Dene Athabaskan language spoken in northeastern British Columbia and the southeastern Yukon, have incorporated videoconferencing technology into their long-distance language documentation, revitalization and education practices. Many speakers and communities of endangered, Indigenous, and minority languages who live in remote regions are at a disadvantage simply because of their remoteness, which has limited their ability to access funding, form partnerships and work with language researchers. In turn, historically such Indigenous languages — their speakers, their stakeholders and their projects — have been under-resourced. This thesis discusses how a team of Kaska language workers have used a professional videoconferencing platform to regularly engage in long-distance collaborative language projects between Watson Lake, Yukon, and Vancouver, British Columbia. While language projects often focus either on documentation or revitalization of a language, in these videoconferencing sessions project collaborators are able to integrate these two activities. The incorporation of this technology in their language work has had several positive by-products for project collaborators, including strengthened personal relationships, a heightened sense of connectedness to language, land and each other, and an interdependence on each other that also distributes authority, all of which have formed a community of practice that has made this language team into invested collaborators. Ultimately, this research suggests that in certain circumstances, videoconferencing technology can be used to support language documentation, revitalization and education, as well as the people who undertake such projects, in a myriad of ways that extends beyond the intended outputs of the projects themselves.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.048 | 0.031 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".