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Record W4414119684 · doi:10.29173/crossings309

Amnesty International’s “Stolen Sisters” Report: Martyrdom and Unintended Challenges of Life Narratives

2025· article· en· W4414119684 on OpenAlexaffabout

Bibliographic record

VenueCrossings An Undergraduate Arts Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAmnestyNarrativeIndigenousInnocenceStorytellingEmpathyHarmPoliticsRepresentation (politics)

Abstract

fetched live from OpenAlex

This paper examines the efficacy and ethical implications of using life narratives in representing the Missing and Murdered Indigenous Women and Girls crisis (MMIWG) in Canada, focusing specifically on Amnesty International's 2004 "Stolen Sisters" report. Through a critical analysis informed by Tuck and Yang's theoretical framework on refusal in research and decolonial pedagogy, this study argues that while third-person life narratives can be effective tools for raising awareness, they risk perpetuating harm through unintentional political martyrdom. The paper demonstrates how these narratives, though well-intentioned, can facilitate settler moves to innocence through conscientization, where empathy becomes a substitute for actionable change. Furthermore, the analysis reveals how such representations can lead to revictimization and reduction of Indigenous women to mere symbols of a cause, ultimately undermining genuine decolonial efforts. The research concludes that alternative approaches, particularly first-person narratives and Indigenous kinship-based storytelling methods, may offer more ethical and effective means of representation while avoiding the pitfalls of martyrdom and exploitation. This study contributes to broader discussions about ethical representation in Indigenous studies and advocates for a shift toward more community-centered and consent-based narrative practices.

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 categoriesnone
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 score0.477
Threshold uncertainty score0.961

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 studies0.0010.001
Scholarly communication0.0010.001
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 head0.046
GPT teacher head0.343
Teacher spread0.297 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

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