Imagining University/Community Collaborations as Third Spaces to Support Indigenous Language Revitalization
Bibliographic record
Abstract
This paper explores the development of language instruction programs in universities to support Indigenous language revitalization. Eleven Indigenous educators shared rich insights through interviews. Their visions called for language learning that is functional, inseparable from land-based learning, and within multigenerational learning environments led by Elders. Building on these visions, the authors imagined a third space—an Indigenous-led, in-between space—to discuss the potentialities for universities and local communities to come together. The discussion offers strategies for a third space where universities support language revitalization in communities through co-programming, community-based courses in functional, immersive settings guided by Elders, and an online site for additional supports.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".