MétaCan
Menu
← Back to cohort
Record W6991019027

Exploring\tIndigenous Youth Incarceration in Canada

2018· dissertation· en· W6991019027 on OpenAlexaffabout

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIndigenousRacismPovertyQualitative researchProduct (mathematics)Qualitative propertyRacial bias
DOInot available

Abstract

fetched live from OpenAlex

This project is focused on the issue of Indigenous youth incarceration in Canada. The primary goal of this study is to explore whether this prevalent problem is a product of social environment, such as poverty and reserve conditions, or if it is rooted in institutionalized racism in Canada. This study has centrally been a literature review of relevant statistical information and academic publications on this topic, as well as qualitative coding of selected sources. I have also utilized the case study of the Flying Dust First Nation in Saskatchewan in order to explore a microanalysis of an Indigenous community that contradicts prevailing negative social patterns in Canada. My central argument is that high rates of Indigenous youth incarceration in Canada are a product of the combined effect of complex social factors, as well as systemic racism in governmental structures. Through my qualitative research, I have found that some of the academic literature has further propagated stereotypical notions of Indigenous Canadians, as well as produced a distinct binary between Indigenous groups and the non-Indigenous, unmarked category of identity.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.076
Threshold uncertainty score0.549

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0160.003
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.246
Teacher spread0.206 · 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe Atrium (University of Guelph)→Same topicIndigenous Health, Education, and Rights→French-language works237,207→