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Record W2798130697 · doi:10.3138/topia.18.89

Revanchism in the Canadian West: Gentrification and Resettlement in a Prairie City

2007· article· en· W2798130697 on OpenAlexvenueaboutno aff
Kara Granzow, Amber Dean

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsGentrificationDowntownContext (archaeology)AppropriationSociologyEthnographyIdeologyIndigenousRedevelopmentPolitical scienceCriminologyHistoryLawArchaeologyCivil engineeringAnthropologyEngineering

Abstract

fetched live from OpenAlex

This paper provides a local, quasi-ethnographic, activist-oriented analysis of the City of Edmonton’s gentrification of an area just adjacent to the downtown core. Announced in 2005 and according to the teachings of self-proclaimed “go-to guru” Richard Florida, City officials have called this a “revitalization” of the “downtown east.” The process and its already apparent effects reveal the strategy as one towards a targeted re-appropriation of land and forced displacement of people in the interest of accumulating first cultural but ultimately financial capital. Through analysis of the City of Edmonton’s public meetings, as well as through the analysis of materials provided at those meetings and on City websites, we offer a critique of the City’s process, we debunk Florida’s “creative class” ideology, and finally, most importantly, we situate the gentrification of Edmonton’s downtown within the historical and local context. The particular process of gentrification of this particular Western Canadian city is congruent with the “original” and continual theft of the land from indigenous inhabitants. It also must be considered in light of the cases in which women, mostly aboriginal, have gone missing or been murdered, sometimes from this very neighborhood. Edmonton’s “revitalization” process hides its gentrification and hides an utter inattention to historical parallels and to contemporary issues of urgency.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.353
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2007
Admission routes2
Has abstractyes

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