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Record W2742916987 · doi:10.1080/02723638.2017.1360038

Assembling “Japantown”? A critical toponymy of urban dispossession in Vancouver, Canada

2017· article· en· W2742916987 on OpenAlexafffundabout
Trevor Wideman, Jeffrey R. Masuda

Bibliographic record

VenueUrban Geography · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of British ColumbiaRoyal Canadian Geographical Society
KeywordsToponymyDowntownMateriality (auditing)ScholarshipTRACE (psycholinguistics)Assemblage (archaeology)Citizen journalismHistoryUrbanizationSociologyGenealogyGeographyArchaeologyPolitical scienceLinguisticsAestheticsLawArt

Abstract

fetched live from OpenAlex

Geographic scholarship in critical toponymy has highlighted the importance of place naming as a form of discursive power within processes of urbanization. This paper builds on such literature and advances a novel theory of toponymic assemblage to interpret findings from a participatory research project in the Downtown Eastside of Vancouver, Canada. We foreground neighborhood history in the form of a Japanese Canadian enclave and its wartime uprooting and dispossession, and trace the historical antecedents of a resurrected toponymy of “Japantown” that has appropriated and renarrated Japanese Canadian history to facilitate further rounds of dispossession. Using a genealogical method, we highlight three “moments” of Japanese Canadian uprooting, return, presence, and activism, demonstrating how toponymies are assembled in place in heterogeneous and historically contiguous ways. This approach expands on current research in critical toponymy, offering a novel methodology for exploring the enrolment of toponymy, discourse, and materiality in the formation of place.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.076
Threshold uncertainty score0.552

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0420.028
Scholarly communication0.0090.003
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.326
Teacher spread0.306 · 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

Citations22
Published2017
Admission routes3
Has abstractyes

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