Urban transformation and Indigenous-settler reconciliation: Discursive (dis)connections between municipal reconciliation strategies and area redevelopment plans in five Canadian cities
Bibliographic record
Abstract
Municipalities in settler colonial states are currently engaged in seemingly conflicting projects of urban redevelopment and Indigenous-settler reconciliation, given the role the former plays in reproducing colonial dispossession. However, state-led reconciliation itself can also reinforce the settler colonial relationship through its selective recognition of colonial violence, Indigenous presence, and new pathways forward. In response to these tensions, this article examines how emerging municipal reconciliation discourses are reproduced, transformed, or ignored within new area redevelopment plans, and the extent to which this “dialogue” indicates discursive shifts to settler planning norms and ideals. A textual analysis of reconciliation documents and redevelopment plans in five Canadian cities - Vancouver, Edmonton, Regina, Hamilton, and Montréal – reveals that reconciliation discourses of relationship-building, Indigenous presence, and unity and inclusion are consistently translated in ways that maintain the settler planning status quo. Crucially, these concepts are transformed in interaction with planning discourses of urban authority, redevelopment as capital accumulation, and the inclusive city, underlining the need to challenge both capitalist planning norms and unnuanced ‘inclusion’ as a response to inequitable development outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.030 | 0.028 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".