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Record W3107500044 · doi:10.18584/iipj.2020.11.4.8215

A Journey of Doing Research “In a Good Way”: Partnership, Ceremony, and Reflections Contributing to the Care and Wellbeing of Indigenous Women Living with HIV in Canada

2020· article· en· W3107500044 on OpenAlexaffvenueabout
Doris Peltier, Carrie Martin, Renée Masching, Mike Standup, Claudette Cardinal, Valerie Nicholson, Mina Kazemi, Angela Kaida, Laura Warren, Denise Jaworsky, Laverne Gervais, Alexandra de Pokomandy, Sharon Bruce, Saara Greene, Marissa Becker, Jasmine Cotnam, Kecia Larkin, Kerrigan Beaver, Carrie Bourassa, Mona Loutfy

Bibliographic record

VenueInternational Indigenous Policy Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of SaskatchewanMcGill UniversityMcMaster UniversityWomen's College HospitalUniversity of Northern British ColumbiaUniversity of ManitobaSimon Fraser UniversityMohawk CollegeCAAN Communities, Alliances & Network
Fundersnot available
KeywordsCeremonyIndigenousGeneral partnershipStorytellingSociologyGender studiesCommissionPower (physics)Political scienceNarrativeLawHistoryArtArchaeology

Abstract

fetched live from OpenAlex

The relationship between the First Peoples of Canada and researchers is changing as processes of self-determination and reconciliation are increasingly implemented. We used storytelling and ceremony to describe a historic event, the Indigenous Women’s Data Transfer Ceremony, where quantitative data of 318 Indigenous women living with HIV were transferred to Indigenous academic and community leaders. Relationship building, working together with a common vision, the Ceremony, and the subsequent activities were summarized as a journey of two boats. The Truth and Reconciliation Commission of Canada's Calls to Action and Indigenous ethical principles were central to the process. The article ends with team members’ reflections and the importance of shifting power to Indigenous Peoples in regard to data collection, their stories, and the resulting policies.

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)0.003
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 score0.041
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
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 head0.043
GPT teacher head0.381
Teacher spread0.337 · 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

Citations16
Published2020
Admission routes3
Has abstractyes

Explore more

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