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Record W7111497268

The Politics of Confinement: Indigenous Homelands, Carceral Imperialism, and the Making of the Deep North

2022· article· en· W7111497268 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPoliticsColonialismState (computer science)First nationMaking-ofIdentification (biology)
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines Indigenous and settler-colonial geographies and politics, and their relationship to processes of incarceration and other forms of confinement. Anchored in Očhéthi Šakówiŋ history, it examines carceral state formation within a broader analysis of sites that promoted Indigenous geographic and bodily confinement by the U.S. and Canadian settler states during the nineteenth and twentieth centuries. These sites included military forts and jails, reservations and reserves, and boarding and residential schools. The geographic area of the study is a place Indigenous peoples call the Deep North, a cross-border region comprised of hundreds of Indigenous homelands, four U.S. states (Montana, North Dakota, South Dakota, Minnesota), and three Canadian provinces (Alberta, Saskatchewan, Manitoba). This region has the highest rates of Indigenous incarceration on the continent. Using historical and critical methodologies, this project demonstrates that while incarceration of Indigenous peoples in North America resembles the incarceration of other minoritized peoples, it has a distinct historical genealogy that can be traced to coercive colonial practices designed to eliminate Indigenous lifeways, knowledge systems, and tribal identification to dispossess Indigenous lands.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.188
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.031
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueDigital Access to Scholarship at Harvard (DASH) (Harvard University)Same topicIndigenous Health, Education, and RightsFrench-language works237,207