Mapping for Awareness of Indigenous Stories
Bibliographic record
Abstract
Joseph Kerski has identified five converging global trends—geo-awareness, geo-enablement, geotechnologies, citizen science, and storytelling—which contribute to the increased relevance of geography for education and society. While these trends are discussed by Kerski in the context of the proliferating significance of geography in teaching and education, they also provide a useful lens for considering the increasing ubiquity of critical approaches to cartography both in general and in the context of teaching and education, where mapping can include participatory collaborations with individuals from a variety of knowledge communities and extend to the mapping of experiences, emotions, and Indigenous perspectives. In this paper, we consider these trends and related ideas such as Kerski’s “geoliteracy” and metaliteracy in light of some relatively current examples and in light of the evolution of research and teaching linked with a series of interrelated map-based projects and courses that take a multidimensional approach to teaching and learning about the Residential Schools Legacy in Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".