On the Importance of Language: Reclaiming Indigenous Place Names at Wasagamack ᐘᕊᑲᒪᕁ First Nation, Manitoba, Canada
Bibliographic record
Abstract
This research focused on utilizing Geographical Information Systems (GIS) and mapping to document over 500 local Indigenous place names at Wasagamack First Nation, Manitoba, and to explore the role of mapping tools in preserving and revitalizing local language and culture. Video interviews with local elder Victor Harper, highlight mapping and GIS as valuable, adaptable, and effective tools in supporting community priorities including: language revitalization, use of syllabics, land-based learning, connecting with the ancestral land, and land-use planning. His interviews, as well as the literature review, share the context of this work countering the immense impact of colonialism and the atrocities of residential school system, which forced a break in the natural order of Indigenous knowledge transfer. This research highlights and records the work of these elders, educators and land use planners in their efforts to reclaim not only local Indigenous place names, but their language and culture. Additionally, the process of mapping local Indigenous place names and including them as part of Manitoba’s Geographical Names Data Base, increases the likelihood that Wasagamack’s Anishinimowin language will enter the mainstream lexicon of Canadian society. The research further indicates that tools like mapping and GIS can have a positive impact on safeguarding language and culture, providing a permanency to knowledge that is otherwise retained orally among elders and at great risk of being lost to 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".