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Record W4413448532 · doi:10.1016/j.cities.2025.106350

Urban transformation and Indigenous-settler reconciliation: Discursive (dis)connections between municipal reconciliation strategies and area redevelopment plans in five Canadian cities

2025· article· en· W4413448532 on OpenAlexafffundabout
Margaret Ellis‐Young

Bibliographic record

VenueCities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsRedevelopmentIndigenousUrban regenerationTransformation (genetics)Political scienceEnvironmental planningGeographyPublic administrationLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.757

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0300.028
Scholarly communication0.0100.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.023
GPT teacher head0.278
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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