Indigenous Toponyms as Pedagogical Tools: Reflections from Research with Tl’azt’en Nation, British Columbia
Bibliographic record
Abstract
Apart from conventional understandings of its utilitarian function as spatial labels (often eponymous in character), toponymy is seldom appreciated as palimpsest or for the layers of meaning it assumes, conveyed in place-name etymologies and local knowledge associated with the named places. Over the years, a growing body of literature has emerged on the use of toponymy in several research fields: the range spans from linguistic investigations into place-names and naming practices to the use of place-names in tracking environmental change, locating places of archaeological interest and understanding the knowledge possessed by local communities about the natural environment. The latter focus describes place-names research with Tl’azt’en Nation, the Dakelh-speaking people whose territory lies in the Stuart-Trembleur watershed of central British Columbia, Canada. From the perspective that indigenous place-names communicate knowledge about the natural world, indigenous language and(oral) history, this paper will draw upon examples of Dakelh place-names to put forth the argument that toponymy should be considered in curriculum not only as a means to educate about local geography and history, but to instill awareness and appreciation of, as in the case of indigenous place-names, other epistemologies or non-western ways of understanding the world.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".