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Record W4381747867 · doi:10.36642/mjil.44.3.rebraiding

ReBraiding Frayed Sweetgrass for Niijaansinaanik: Understanding Canadian Indigenous Child Welfare Issues as International Atrocity Crimes

2023· article· en· W4381747867 on OpenAlexaboutno aff
Alyssa Couchie

Bibliographic record

VenueMichigan Journal of International Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGenocideCriminologyWelfarePolitical scienceInternational lawColonialismLawSociologyEcology

Abstract

fetched live from OpenAlex

The unearthing of the remains of Indigenous children on the sites of former Indian Residential Schools (“IRS”) in Canada has focused greater attention on anti-Indigenous atrocity violence in the country. While such increased attention, combined with recent efforts at redressing associated harms, represents a step forward in terms of recognizing and addressing the harms caused to Indigenous peoples through the settler-colonial process in Canada, this note expresses concern that the dominant framings of anti-Indigenous atrocity violence remain myopically focused on an overly narrow subset of harms and forms of violence, especially those committed at IRSs. It does so by utilizing a process-based understanding of atrocity and genocide that helps draw connections between familiar, highly visible, and less recognized forms of atrocity violence, which tend to be overlapping and mutually reinforcing in terms of their destructive effects. This process-based understanding challenges the neocolonial, racist, and discriminatory attitudes reflected in the drafting and interpretation of the Genocide Convention and other atrocity laws that ignore the lived experiences of subjugated groups. Utilizing this approach, this note argues that, as applied to Indigenous populations, Canada’s longstanding discriminatory child welfare practices and policies represent an overlooked process of anti-Indigenous atrocity violence. Only by understanding current child welfare challenges facing Indigenous communities as interwoven with longstanding anti-Indigenous atrocity processes, such as the IRS system, can we understand what is at stake for affected communities and fashion appropriate remedies in international and domestic law.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.047
GPT teacher head0.334
Teacher spread0.287 · 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 designTheoretical or conceptual
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

Citations2
Published2023
Admission routes1
Has abstractyes

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