Cybercartography and education: research and teaching with the <i>Residential Schools Land Memory Atlas</i>
Bibliographic record
Abstract
Abstract This paper sheds light on intersections between teaching and research in the Cybercartographic Residential Schools Land Memory Atlas (RSLMA), which is the central output of the Residential Schools Land Memory Mapping Project (RSLMMP). Building on previous work in Cybercartography, the RSLMMP has further contributed to the integration of research and education and the emergence of new research and education relationships. Viewing the atlas as a project output comprised of iterative processes along multiple dimensions allows us to appreciate limitations as challenges for further iterations, including new related projects and ongoing volunteer work with students. In addition to participating in the national response to the Truth and Reconciliation Commission of Canada’s Calls to Action, this project – including the atlas – provides a model for a unique blend of teaching and research and the basis for further and new collaborations with a variety of different partners, including Residential School survivors. As a reconciliation project, the Residential Schools Land Memory Atlas further contributes to the intercultural bridge building aims of its parent, the Lake Huron Treaty Atlas , as it forges on in new directions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".