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Record W2389504348

More than Stone and Iron: Indigenous History and Incarceration in Canada, 1834-1996

2016· article· en· W2389504348 on OpenAlexaboutno aff
Seth Adema

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGeographyPolitical scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines Indigenous (First Nation, Métis, and Inuit) history as played out in Canadian prisons. It argues that in the prison, processes of colonialism, decolonization, and neocolonialism took place simultaneously. In the nineteenth century, the prison was built as part of a network of colonial institutions and polices. It was imagined, designed, and built by representatives of the Canadian state alongside other colonial institutions, drawing on similar intellectual traditions. It maintains the imprint of this colonial origin. Prisons also became arenas for Indigenous cultural exchange and cultural creation, which in most cases subverted the logic of the prison. This was part of a larger effort at decolonizing the prison. In the twentieth century, Indigenous prisoners actively challenged the colonial logic of the prison by affirming their Indigenous cultures and identities. As Indigenous inmates expressed their cultural identities in prisons, they created literary, material, and ceremonial cultural frameworks distinct to the prison yet reflective of the wider Canadian context. Still, colonial practices emerged in new ways, in a process described in this dissertation as neocolonialism. By drawing on oral and archival sources, this dissertation demonstrates the complexity behind these historical processes of colonization, decolonization, and neocolonialism in Canada, while shedding light on the nature of the prison system and Indigenous history.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

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