‘Process Toponymy’: A GIS-Based Community-Engaged Approach to Indigenous Dynamic Place Naming Systems and Vernacular Cartography
Bibliographic record
Abstract
This paper discusses the aim and the process of designing a community-engaged open-access GIS toponymic platform, based on Indigenous Evenki place names. Most projects on Indigenous toponymy available online are either oriented towards professional use among scholars or serve as enclosed repositories of Indigenous knowledge. Toponymic atlases remain the most common form of documenting and representing Indigenous place naming systems. Yet, temporal and geographic comparisons of place names have clearly demonstrated that, along with a conventional understanding of Indigenous place names as stable and conservative, there is a dynamic model of place naming to be found in nomadic societies, when the names are not only passed through generations but also modified and created. This finding required a number of methodological approaches regarding how researchers might collect and represent geospatial concepts and place names in nomadic societies, with the use of GIS technology. Our project attempts to approach this issue by creating an open digital platform that combines GIS with Indigenous vernacular cartography, place names, and a great variety of data regarding the meaning and use of toponyms, their evolution, and change. We call this approach a “process toponymy” and advocate for applying a semiotic approach to documenting and representing Indigenous place names’ knowledge via GIS-based platforms.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".