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Record W2913297810 · doi:10.18584/iipj.2019.10.1.2

Canada's Missing and Murdered Indigenous People and the Imperative for a More Inclusive Perspective

2019· article· en· W2913297810 on OpenAlexaffvenueabout
John Hansen, Emeka Eugene Dim

Bibliographic record

VenueInternational Indigenous Policy Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousPopulationRacismInclusion (mineral)SociologyGender studiesCriminologyPerspective (graphical)Demography

Abstract

fetched live from OpenAlex

The National Inquiry into Missing and Murdered Indigenous Women and Girls (MMIWG) emerged to bring attention to the overrepresentation of missing and murdered Indigenous women in Canada. It has raised awareness about systemic racism and sexism as well as social and economic conditions experienced by Canada's Indigenous population. Yet, research shows that Indigenous males are the most likely to be murdered in Canada (Mulligan, Axford, & Soecki, 2016). Since Indigenous men are going missing and are murdered in disturbing numbers, and they are fathers, brothers, and sons to Indigenous women and girls, it is understandable that many in the Indigenous community wanted to include them in the inquiry. Our analysis explores how the MMIWG and discourses about inclusion and exclusion have been framed in ways that limit interpretations about the root causes of problems experienced by Indigenous people, especially when they exclude an important part of the Indigenous population—Indigenous males. We draw upon Indigenous perceptions of the inquiry and analyses of social norms and stereotypes in order to explore the conflicting positions and experiences associated with missing and murdered Indigenous people in Canada. We conclude by exploring the need for a more comprehensive inquiry. We recognize that a holistic model of inquiry that honours the voices of Indigenous communities is crucial to a proper investigation into missing and murdered Indigenous people in Canada.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.343
Teacher spread0.334 · 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.

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

Citations24
Published2019
Admission routes3
Has abstractyes

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