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Record W4390104901 · doi:10.33524/cjar.v22i3.580

Murky Waters: An Arts-Based Inquiry into Murdered and Missing Indigenous Women and Girls

2022· article· en· W4390104901 on OpenAlexaffvenueabout
Katya Adamov Ferguson

Bibliographic record

VenueThe Canadian Journal of Action Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousAction researchThe artsSociologyEconomic JusticeAction (physics)Power (physics)Gender studiesColonialismPedagogyMedia studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

The Forks in Winnipeg, Manitoba is the central site of this study which un/covers the potentialities of combining critical place inquiries and a/r/tography to mobilize Calls for Justice and education around displacement and disappearance of Indigenous Women and Girls. This study reflects part of my doctoral research which connects living, action-oriented inquiries utilizing earth-based art/works as public provocations. In this contribution, I share how my artistic process of encircling the Missing and Murdered Indigenous Women and Girls (MMIWG) monument with an intricate soil spiral and my encounters with women’s stories and educational resources from the National Inquiry have uncovered complexities of the ongoing disappearance and erasure of women. The discourses embedded in place reveal strong connections to settler colonial forces and have drawn attention to the role of the arts in supporting commemoration and reclaiming discourses of power and place. This action-oriented approach aims to find ways to bring important topics around MMIWG into curricular discussions. This study inspired action steps for advancing the Calls for Justice, including the design of walking professional learning experiences for teachers. The study shows how the place at the Forks functions as a framework inspired by the layered histories and geographies of this significant location.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.374
GPT teacher head0.526
Teacher spread0.152 · 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.

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
Published2022
Admission routes3
Has abstractyes

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