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Record W3007754381 · doi:10.1111/cag.12602

To what extent can online mapping be decolonial? A journey throughout Indigenous cartography in Canada

2020· article· en· W3007754381 on OpenAlexafffundvenueabout
Thomas J. McGurk, Sébastien Caquard

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousSketchStorytellingDigital storytellingGeographyCartographyDigital mappingVisual artsWorld Wide WebNarrativeArtComputer scienceEcology

Abstract

fetched live from OpenAlex

In this paper, we describe and reflect upon our journey through Indigenous online mapping in Canada. This journey has been planned according to an academic goal: assessing the potential of online cartography for decolonial purposes. To reach this goal, we have followed methodological directions provided by Indigenous scholar Linda Tuhiwai Smith to review 18 Indigenous web‐mapping sites across Canada. Supported by a series of ten interviews, this content analysis enabled us to sketch some of the contours of contemporary Indigenous cartography. On one hand, Indigenous communities largely control the data that are shared on these websites. They also partially control the way these data are represented through the mobilization of digital storytelling technologies that are better aligned with Indigenous ways of envisioning relationships to places than conventional maps. On the other hand, they do not have much control over the technological aspects of these projects, for which they remain heavily dependent on non‐Indigenous partners. Throughout this journey, we noticed that women's voices remained marginal in most of these mapping projects, but we also identified evidence supporting the idea that these voices are starting to play a vital role in the on‐going effort of decolonizing mapping processes .

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.019
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.242
Teacher spread0.216 · 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 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

Citations31
Published2020
Admission routes4
Has abstractyes

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