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Record W4200183430 · doi:10.1177/09646639211041476

Indigenous parents and child welfare: Mistrust, epistemic injustice, and training

2021· article· en· W4200183430 on OpenAlexaffabout
Robert Leckey, Raphael Schmieder-Gropen, Chukwubuikem Nnebe, Miriam Clouthier

Bibliographic record

VenueSocial & Legal Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndigenousIgnoranceInjusticeWelfareSociologyEconomic JusticeState (computer science)Political scienceGender studiesSocial psychologyEnvironmental ethicsPsychologyLaw

Abstract

fetched live from OpenAlex

The settler state's taking of Indigenous children into care disrupts their communities and continues destructive, assimilationist policies. This article presents the perceptions of lawyers, social workers and judges of how Indigenous parents experience child welfare in Quebec. Our participants characterized those experiences negatively. Barriers of language and culture as well as mistrust impede meaningful participation. Parents experience epistemic injustice, wronged in their capacity as knowers. Mistrust also hampers efforts to include Indigenous workers in the system. Emphasizing state workers’ ignorance of Indigenous family practices and the harms of settler colonialism, participants called for greater training. But critical literature on professional education signals the limits of such training to change institutions. Our findings reinforce the jurisdictional calls away from improving the system towards empowering Indigenous peoples to run services of child welfare. The patterns detected and theoretical resources used are relevant to researchers of other institutions that interact with vulnerable populations.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.413
Threshold uncertainty score0.821

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.015
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.003
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 head0.030
GPT teacher head0.335
Teacher spread0.304 · 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

Citations18
Published2021
Admission routes2
Has abstractyes

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