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Record W4206518032 · doi:10.1590/1809-43412021v18a707

Historical justice and reparation for Indigenous Peoples in Brazil and Canada

2021· article· en· W4206518032 on OpenAlexaboutno aff
Ana Catarina Zema, Clarisse Drummond, Marcelo Zelic, Elaine Moreira

Bibliographic record

VenueVibrant Virtual Brazilian Anthropology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRedressGenocideEconomic JusticeTransitional justicePolitical scienceScope (computer science)Indigenous rightsCriminologyColonialismLawSociologyHuman rights

Abstract

fetched live from OpenAlex

Abstract The struggle of Indigenous Peoples for historical justice and reparation has gained visibility with the truth commissions' work in Brazil and Canada. Their final reports confirmed the Canadian and Brazilian states' responsibility for the genocide of thousands of Indians. To start a reconciliation process, several redress recommendations were made, but never fully accomplished. We observed repeated violence acts against Indigenous Peoples in both countries more than five years after these recommendations were published. The purpose of this article is to evaluate, from the Critical Studies of Transitions’ perspective, the reconciling and reparative scope of the Truth Commissions of Brazil and Canada and to analyze the difficulties of implementing their recommendations. We show that despite promises to transform colonial relations, these Truth Commissions have been unable to address the past and the continuity of structural violence affecting Indigenous Peoples.

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 score0.054
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0270.016
Scholarly communication0.0050.001
Open science0.0020.005
Research integrity0.0020.002
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 head0.012
GPT teacher head0.308
Teacher spread0.295 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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