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Record W3200981669 · doi:10.1177/10778004211042345

Moving Encounters With Spatial Racism: Walking in San Jose Japantown

2021· article· en· W3200981669 on OpenAlexfundno aff
Kimberly Powell

Bibliographic record

VenueQualitative Inquiry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAssemblage (archaeology)RacismSociologyMemorializationAutoethnographyNarrativePoliticsMovement (music)Gender studiesAestheticsHistoryPolitical scienceLawArchaeologyArt

Abstract

fetched live from OpenAlex

In this article, I address how walking as a curatorial practice of storying a neighborhood facilitates an irreducible politics of place occurring as affective intensities at various registers, where everyday movements entangle with spatial enactments of racism. Working with theories of assemblage and immanent movement, I examine walking narratives in San Jose Japantown, California (U.S.), a historic, ethnic neighborhood historically subjected to U.S. government and banking practices of “redlining” and Japanese American incarceration and dislocation to prison camps. As an analytical method, assemblage requires attention to movement: material elements of arrangement, the relations they require, new arranging and arrangements they might enable, and how these arrangements are legitimated. I examine spatial racism as an assemblage, analyzing its affective qualities wherein attentiveness to immanent movement might breach the assemblage and, in doing so, reach toward radical reformation through memorialization, community activism and development.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.415
Teacher spread0.345 · 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.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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