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Record W4403611959 · doi:10.1177/09646639241288075

From European Roots to Settler Soil: Adapting Foucault’s Biopolitics to Canadian Settler Colonialism

2024· article· en· W4403611959 on OpenAlexaffabout
Amy Swiffen

Bibliographic record

VenueSocial & Legal Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsConcordia University
Fundersnot available
KeywordsBiopowerColonialismMichel foucaultGovernmentalitySociologyGender studiesEthnologyPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

This article examines the applicability of Michel Foucault’s biopolitical theory within the Canadian settler colonial context. Foucault’s concept of biopolitics, which describes the shift from sovereign power to the regulation of life and populations, is grounded in European historical contexts. In contrast, the Canadian settler colonial experience diverges significantly, necessitating a re-examination of biopolitics. The article argues that in Canada, the relationship between sovereignty, population, and territory does not follow the European model described by Foucault. Instead, Crown sovereignty established its control through legal mechanisms that defined ‘Indian’ status, displacing Indigenous territorialities and political structures. By analysing the evolution of ‘Indian’ status within the Indian Act (1985), the article highlights the centrality of law in constructing settler colonial biopolitics. This legal framework was used to manage and assimilate Indigenous populations, reflecting a unique biopolitical strategy aimed at creating a relationship to territory and population. The study contributes to a more specific understanding of biopolitics in settler colonial contexts, emphasizing the distinct role of law in these 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 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)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0150.050
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.323
Teacher spread0.285 · 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
Published2024
Admission routes2
Has abstractyes

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