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Record W3091586076 · doi:10.3138/cart-2019-0016

Every Bus Stop a Tomb: Decolonial Cartographic Readings against Literary, Visual, and Virtual Colonial Claims to Space

2020· article· en· W3091586076 on OpenAlexaffvenue
Dallas Hunt

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsColonialismIndigenousSpace (punctuation)Reading (process)ConversationSensibilitySociologyVisual artsArtAestheticsGeographyComputer scienceLiteraturePhilosophyArchaeologyCommunicationEcologyLinguistics

Abstract

fetched live from OpenAlex

This article offers a decolonial reading of digital counter-mapping processes through an engagement with settler artist Sylvia Borda’s art installation, “Every Bus Stop in Surrey, BC.” By putting the theories of Michel de Certeau and Walter Benjamin into conversation with the research and insights of Indigenous feminist theorists Audra Simpson and Mishuana Goeman, I examine how counter-mapping efforts may contribute to and normalize the (settler) colonization of everyday life. I analyze Borda’s cartographic art project in order to illuminate the dispersal and (dis)placement of peoples in and across (virtual) urban spaces and examine how the experiences of these population distributions contribute to resistive artistic projects, and simultaneously how these art installations can reproduce settler colonial erasures, with Borda’s installation being an illustrative example. Using Borda’s text as a representative case, I advocate for the cultivation of a decolonial sensibility when engaging with digital cartography.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.052
Scholarly communication0.0120.004
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.320
Teacher spread0.305 · 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 designTheoretical or conceptual
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

Citations2
Published2020
Admission routes2
Has abstractyes

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