Indigenous place names in arctic Canada: A publicly accessible inventory of projects
Bibliographic record
Abstract
Toponyms contain Indigenous modes of understanding and reflect ecological histories and deep relationships between Indigenous communities, arctic environments, time, and land. Completed toponymic studies are useful for researchers to access; however, they are notoriously difficult to find. Many are completed by community groups and published on their websites, or are completed by government agencies and published as grey literature. An inventory of toponym projects has not existed, and eliciting what has been completed where, with whom, and by whom has required long searches through academic and grey literature. In this paper, we inventory Indigenous toponymy projects in the Canadian North, and document our efforts to produce a publicly accessible index where toponymy projects can be found via maps. New or unknown resources can be added by users. Our purpose, here, is to document the production of this resource and to increase awareness of toponymical resources among communities, researchers, scientists, and other stakeholders. We reflect on knowledge gained through construction of the index and make observations on trends in Inuit toponym research through time. We argue for renewed efforts across arctic sciences to recognize Inuit-environment relationships through reference to place names and the ecological histories they encapsulate, and we provide considerations for future work.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.048 | 0.111 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".