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Record W2442444085 · doi:10.1080/2201473x.2016.1186311

Decolonizing geographies of power: indigenous digital counter-mapping practices on turtle Island

2016· article· en· W2442444085 on OpenAlexafffundabout
Dallas Hunt, Shaun Stevenson

Bibliographic record

VenueSettler Colonial Studies · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousSovereigntyColonialismSociologyContext (archaeology)Power (physics)PoliticsAnthropologyGeographyArchaeologyLawPolitical science

Abstract

fetched live from OpenAlex

This paper addresses the decolonizing potential of Indigenous counter-mapping in the context of (what is now called) Canada. After historicizing cartography as a technique of colonial power, and situating Indigenous counter-mapping as an assertion of political and intellectual sovereignty, we examine the digital map of Amiskwaciwâskahikan (Plains Cree for Edmonton, Alberta) produced by the Pipelines Collective, which overlays Treaty 6 Indigenous maps onto ‘conventional’ maps to denaturalize and challenge colonial renderings of city space. We then discuss the expanding trend of guerrilla mapping techniques engaged in by Indigenous groups, emphasizing the Ogimaa Mikana project in Toronto, wherein Anishinaabemowin names were stickered over settler street names. Expanding the spatial theories of Michel de Certeau and Gilles Deleuze, and drawing on the research and insights of Indigenous scholars Jodi Byrd and Mishuana Goeman, our paper considers how emerging digital counter-mapping efforts offer ambivalent possibilities for Indigenous peoples to assert their presence in material ways.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.655
Threshold uncertainty score0.685

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.016
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.063
GPT teacher head0.392
Teacher spread0.329 · 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

Citations164
Published2016
Admission routes3
Has abstractyes

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