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Record W2169150025 · doi:10.7202/039671ar

Unpacking Settler Colonialism’s Urban Strategies: Indigenous Peoples in Victoria, British Columbia, and the Transition to a Settler-Colonial City

2010· article· en· W2169150025 on OpenAlexvenueaboutno aff
Penelope Edmonds

Bibliographic record

VenueUrban History Review · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismEthnogenesisIndigenousPoliticsModernityPolityMercantilismEthnologyGeographyExpropriationPolitical scienceHistorySociologyAnthropologyPolitical economyArchaeologyEthnic groupLawEcology

Abstract

fetched live from OpenAlex

This article uses settler colonialism as a specific analytic frame through which to understand the historical forces in the formation of settler cities as urbanizing polities. Arguing that we must pay attention to the intertwined histories of immigration and colonization, the author traces the symbolic and economic functions and origins of the settler-colonial city to reveal its political imperatives, the expropriation of Indigenous land, and the dispossession, removal, sequestration, and transformation of Indigenous peoples. Taking as a case study the city of Victoria, BC, and its Lekwungen people throughout the nineteenth century, the author charts the shift from a mixed and fluid mercantilist society to an increasingly racialized and segregated settler-colonial polity. This transition reveals how bodies and urbanizing spaces are reordered and remade, and how Indigenous peoples come to be produced and marked by political categories borne of the racialized practices of an urbanizing settler colonialism, which complement the powerful forces of settler ethnogenesis and colonial modernity.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.096
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0050.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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

Citations61
Published2010
Admission routes2
Has abstractyes

Explore more

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