Bibliographic record
Abstract
Indigenous place names contain knowledge of the landscape and encode unique perceptions of landscapes with which Indigenous Peoples have interacted for hundreds, often thousands, of years. However, many Indigenous place names have been lost as a result of colonization. Furthermore, many of these have been replaced with colonial place names, and their loss contributes to overall language attrition. In turn, the loss of language makes it difficult, or even impossible, to understand the concepts embedded within Indigenous place names that do remain in use. The documentation and conservation of place names is thus an important aspect of Indigenous language preservation and revitalization that can help facilitate reconnection with the language and the land. This paper outlines the Atlas of Kanyen'kehá:ka Space digital atlas project, an initiative that uses digital mapping to aid in the documentation and revitalization of the Kanyen'kéha (Mohawk) language through community participatory mapping of Kanyen'kéha place names and landscape-related language. It describes the initial stages of the Atlas of Kanyen'kehá:ka Space project, including its theoretical framework, the O'nonna model, and its community-based participatory methodology for digital mapping. It reports on a series of mapping workshops within three Kanyen'kehá:ka communities and shares initial findings and future directions for the project.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.015 |
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".