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Record W2897212361 · doi:10.15273/jue.v8i2.8688

Beyond Invisibility: A REDress Collaboration to Raise Awareness of the Crisis of Missing and Murdered Aboriginal Women

2018· article· en· W2897212361 on OpenAlexaffvenueabout
Natalie Marie Lesco

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsRedressInvisibilityGrassrootsIndigenousOppressionParticipatory action researchSociologyCitizen journalismPoliticsAction (physics)Political scienceGender studiesLawPublic relationsAnthropology

Abstract

fetched live from OpenAlex

This article describes the impact of a case study of the REDress project on a university campus in Nova Scotia, Canada. The REDress project is a grassroots initiative that operates at the local level to empower Aboriginal women through an evocative art exhibit: the hanging of empty red dresses symbolizing missing and murdered Indigenous women and girls and the emptiness of the societal response to the violence committed against them. Using a participatory-action research model (PAR), which guides the exploration of the kinds of ideas instilled within this community-based initiative, my research demonstrates the potential this project has to mobilize local Indigenous women’s perspectives and voices, in order to break the silence to which they are often subjected through structures of oppression. This process relies on the establishment of meaningful connections with members of the StFX Aboriginal Student’s Society and creating a transparent research process, while also encouraging action in the form of awareness building. The project makes a political statement that resists the ascribed invisibility of Aboriginal women by honouring the lives of missing and murdered Aboriginal women. As a community-based initiative, the REDress project demonstrates the beginnings of reconciliation by cultivating meaningful relationships that provide hope for the future.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.775

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.352
Teacher spread0.311 · 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 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
Published2018
Admission routes3
Has abstractyes

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