Tracing the Flow of Treaty Relations in the Salish Sea
Bibliographic record
Abstract
Cartography, as an ever-evolving discipline, necessitates ongoing critical engagement with the question, “Who is the map for?” This inquiry is particularly pertinent in addressing the colonial narratives that have historically shaped mapping practices, especially in ways that relationships between First Nations peoples and the state – such as treaties, reserves/reservations, and land claim areas -- are represented. The Coast Salish Peoples, the original Nations of the Salish Sea, maintain teachings, laws, governance systems, and land tenure practices that predate colonial settlement and continue to thrive today. In the Salish Sea bioregion (similar but not identical to the territories of Coast Salish peoples), historic and modern-day treaties have established foundational relationships between Indigenous communities and the settler states of Canada and the United States. The transboundary nature of the Salish Sea complicates conventional mapping practices, as political, legal, and geophysical boundaries often fail to align with how Indigenous peoples themselves see and practice their territorialities. As non-Indigenous scholars who are committed to the goals outlined by the Truth and Reconciliation Commission (including Call to Action 62), we aim to take on work to educate the public on treaties. Invited by Associate University Librarian, Reconciliation Ry Morran (also the founding director of the National Centre for Truth and Reconciliation) to contribute to a large, highly visible public installation at the University of Victoria’s McPhearson Library, that goes beyond land acknowledgements; to evoke the geography of treaty relations of the lək̓ʷəŋən peoples on whose traditional territory the university stands in broader context of the varied treaty relations. Through a process of cartographic reclamation, our map is informed by scholars in the field of decolonial cartography (Lucchesi, 2020; Tucker & Rose-Redwood, 2015, Thom, 2009), and puts dialogue into practice to emphasize interconnectedness, and point out challenges of dominant power structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".