Revitalizing Indigenous Languages in Toronto: The Responsibilities and Potential Roles of non-Indigenous Peoples
Bibliographic record
Abstract
In the place known as Toronto, Indigenous peoples are dedicating themselves to revitalizing and reclaiming their languages. How can the non-Indigenous peoples of this place end the neglect and assault on Indigenous languages and support their goals? I spoke with ten, mostly First Nations, people who are engaged with languages in the Toronto area to ask for guidance. I learned about the different kinds of relationships and responsibilities that hold us together, including wampum belt treaties, reconciliation, our relationship with the land, and for some, common experiences of colonization or language oppression. I learned about the roles that non-Indigenous peoples can play, including supporting Indigenous community-based organizations, political action and land back, raising awareness and visibility, removing barriers, challenging stereotypes and assumptions, and perhaps learning the languages. This research is intended as a foundation for public education to stimulate informed solidarity and action in Indigenous language revitalization.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".