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Record W4229002145 · doi:10.1111/soc4.12984

Indigenous women, multiple violences, and legal activism: Beyond the dichotomy of human rights as “law” and as “ideas for social movements”

2022· article· en· W4229002145 on OpenAlexafffund
Paulina García‐Del Moral

Bibliographic record

VenueSociology Compass · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousHuman rightsSocial movementSociologyConversationInternational human rights lawScholarshipLawIndigenous rightsColonialismFace (sociological concept)Gender studiesPolitical scienceSocial sciencePolitics

Abstract

fetched live from OpenAlex

Abstract This review interrogates the divide between human rights “as ideas for social movements” and human rights “as law” that permeates the literature on human rights law and gender violence by putting it into conversation with the scholarship on Indigenous women's legal activism against the multiple forms of violence that they face. This divide obscures the ways in which Indigenous women across the Americas have appropriated and re‐signified the discourse and practice of human rights by engaging in formal legal processes at the community, domestic, and supranational levels. I problematize this dichotomy and argue that human rights “as ideas for social movements” and “as law” go hand in hand to challenge the multiple injustices affecting the lives of Indigenous women. This review invites sociologists to consider the experiences of Indigenous women's legal activism and its relationship to colonialism in their analyses of social movement dynamics and it contributes to decentering analyses on legal mobilization that are based on the global North.

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)0.003
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: none
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.019
Scholarly communication0.0060.003
Open science0.0010.003
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 head0.016
GPT teacher head0.319
Teacher spread0.303 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

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