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Record W4412395672 · doi:10.1080/24694452.2025.2510701

(Re)Making Native Space: Locating Gendered Geographies of Law, Territoriality, and Dispossession in the Colonial Archive

2025· article· en· W4412395672 on OpenAlexafffund
Sarah Hunt

Bibliographic record

VenueAnnals of the American Association of Geographers · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsUniversity of Victoria
FundersCanada Research Chairs
KeywordsTerritorialityColonialismSpace (punctuation)Political scienceSociologyEconomic geographyGeographyLaw

Abstract

fetched live from OpenAlex

Revisiting Cole Harris’s Making Native Space, this article responds to Harris’s assertion that settler attitudes toward Indigenous people were not gendered but that, rather, it was the civilization–savagery paradigm that conditioned Indigenous dispossession. Revisiting the same colonial archive of British Columbia through the dual lenses of legal geography and Indigenous feminism, the article examines available evidence about Indigenous women’s role in land governance in British Columbia. Although fragmented and partial, due to the historical and colonial devaluation of Indigenous women’s knowledge, evidence is available on the specific forms of land governance throughout northern, interior, and coastal regions of British Columbia, forming what might be considered property relations in colonial terms. Through a rereading of the archive with knowledge of Indigenous gender and legal relations, it is argued that settlers were able to suppress the legal nature of women’s land governance practices through colonial understandings of Indigenous women as racial and gendered subjects who were already always deterritorialized, incapable of forming territorial expressions of authority. The suppression of Indigenous women’s authority is continued in settler-colonial paradigms that separate traditional ecological knowledge from realms of law or governance and that deny gender as a structural component of settler colonialism.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.365
Teacher spread0.339 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

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