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Record W6981977440

The geospatial web, geospatial ontologies, and Eastern Cree conceptualizations of space and time

2019· dissertation· en· W6981977440 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeospatial analysisIndigenousOntologyVisionSemantic WebGeographic information systemLinked data
DOInot available

Abstract

fetched live from OpenAlex

Maps and mapping technologies have been criticized for contributing to Indigenous assimilation.For example, explorers and colonizers represented Indigenous lands as unoccupied and free,ignoring traditional place names and existing communities. In recent years, a large body ofresearch has developed to decolonize maps; today, Indigenous communities across the world areusing maps and mapping technologies to support their causes. However, the underlyingarchitecture of mapping technologies can still be at odds with Indigenous ways of knowing andconceptualizing the world. For example, GIS and spatial data structure mostly consider time aslinear, whereas Indigenous conceptualizations often view time as cyclical.Advances in mapping technologies over recent years with the geospatial web (geoweb)have marked dramatic changes in traditional cartographic practices and conventional geographicinformation systems (GIS). This dissertation asks whether the underlying architecture of thegeoweb is effective in considering Indigenous ontologies and epistemologies. The aim of thisresearch is to inform next generations of mapping technologies that will assist Indigenous peoples in the expression of their knowledge and of visions of their territory within their ownepistemological and ontological frameworks.In this dissertation, I first address the benefits and challenges of the geoweb inconsidering Indigenous epistemologies. I review the literature on the critiques of existingIndigenous GIS technologies, and compare these critiques with new components in thearchitecture of the geoweb to evaluate the changes. I argue that, in many ways, the geoweb is notadequately addressing the shortcomings identified in GIS technologies. After, I address thequestion of ontological benefits and challenges of geospatial technologies. I review the literatureon conventional geospatial ontologies and look at assumptions of universality. I show thatIndigenous ontologies prove universal assumptions embedded in existing technologies to bewrong.These literature reviews point to the need to develop Indigenous geospatial technologiesthat are place-based instead of universal. My research addresses this need by focusing ondeveloping a spatio-temporal ontology based on Eastern Cree concepts. The case study takesplace in the Cree Nation of Wemindji in Norther Quebec. I present the methodological approachused in this research and discuss my positionality. I then present the results of the research andpropose an alternate ontology to that found in conventional geospatial technologies to betterconsider Indigenous concepts of time and space.

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)0.006
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0050.062
Scholarly communication0.0110.025
Open science0.0020.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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