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Record W3132631602 · doi:10.22215/etd/2020-14307

Investigating Indigeneity within Incarceration: Healing Lodges in the Canadian Media

2020· dissertation· en· W3132631602 on OpenAlexaboutno aff
Natalia Manning

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismSovereigntyIdentity (music)NarrativeIntersectionalityGender studiesSociologyPunishment (psychology)Political scienceMedia studiesPoliticsAestheticsArtPsychologySocial psychologyLawEcology

Abstract

fetched live from OpenAlex

My research examines the media portrayals of Indigenous healing lodges within Canada, through a critical discourse analysis of relevant Canadian news media sources, spanning from 2009 to 2019. I utilize both intersectionality and settler colonialism as my theoretical approaches for this research to contextualize media discourses to the longstanding history of colonialism in Canadian society, as well as intersections of identity. I also analyze research questions relevant to settler versus Indigenous-led media, as well as the constructions of healing lodges as a form of punishment and the ways in which Indigenous offenders were depicted. In addition, I explore how narratives about healing lodges further solidified claims regarding Indigenous sovereignty. I conclude with a multi-faceted approach moving forward through support for the implementation of more Indigenous-led healing lodges, as well as cultural resurgence, as advocated by a number of prominent Indigenous scholars in the field.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.062
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.010
Science and technology studies0.0270.021
Scholarly communication0.0160.004
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.337
Teacher spread0.286 · 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
Published2020
Admission routes1
Has abstractyes

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