A “Living” Atlas for Geospatial Storytelling: The Cybercartographic Atlas of Indigenous Perspectives and Knowledge of the Great Lakes Region
Bibliographic record
Abstract
This article presents and discusses the cultural and technological contexts of the development of the Cybercartographic Atlas of Indigenous Perspectives and Knowledge of the Great Lakes Region in Ontario. The atlas was developed to enhance the capability to recover the systemic nature of traditional Indigenous knowledge by electronically interrelating different forms of expressive culture (language, oral traditions, items of material and visual culture, historical documentation). To reach this goal, this atlas includes a “living” geospatial database that serves as an artefact repository and enables communities to contribute geographically relevant knowledge and to develop their own interactive, multimedia online geospatial stories through modules or sections. Two of these modules are discussed here: a treaties module focusing on the survey phase of the Lake Huron treaty process, and a culture module geared toward engaging Aboriginal artists, community members, and high school students in contributing to the development of this community-based atlas. The discussion concludes with a critical look at the potential of cybercartography and the challenges that remain, especially when it comes to further developing the “living” and the collaborative dimensions of cybercartographic atlases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".