Refusing to relinquish: How settler Canada uses race, property, and jurisdiction to undermine urban Indigenous land reclamation
Bibliographic record
Abstract
Critiques of settler colonial urbanism have paid close attention to the political work that property and racism do in materializing settler colonial cities and naturalizing settler control over urban land and resources. We contribute to these debates by examining how the co-production of property and race intersects with jurisdiction to secure white possession against the demands of an urban Indigenous land reclamation in Canada’s national capital. Drawing on an analysis of government records obtained using Access to Information and Privacy requests, key informant interviews, and a three-year engagement with land defenders and allies, we demonstrate how property and jurisdiction carved the contested space into distinct spheres of settler governing authority. The need to confront the singularity of each governing authority on its own terms made it impossible to directly contest ongoing dispossession as a singular process involving the entire site. Instead, organizers and activists were forced to fight for separate pieces of land, dividing limited time, energy, and resources across multiple facets of a settler colonial structure of invasion. We argue that this process of jurisdictional fragmentation, which organized the co-production of property and race in defence of white possession, can be productively understood as a process of fortification.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.038 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".