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Record W3164594747 · doi:10.23889/ijpds.v6i1.1386

Indigenizing our Research

2021· article· en· W3164594747 on OpenAlexaffabout
Valerie Nicholson, Andreea Bratu, Alison R. McClean, Simran Jawanda, Niloufar Aran, Knighton Hillstrom, Evelyn Hennie, Claudette Cardinal, Elizabeth Benson, Kerrigan Beaver, Anita C. Benoit, Denise Jaworsky

Bibliographic record

VenueInternational Journal for Population Data Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of TorontoUniversity of New BrunswickWomen's College HospitalUniversity of Northern British ColumbiaUniversity of British ColumbiaAIDS Vancouver
Fundersnot available
KeywordsIndigenousParticipatory action researchCitizen journalismCommunity-based participatory researchPublic relationsSociologyPopulationPolitical scienceEcology

Abstract

fetched live from OpenAlex

The use of data intensive health research has allowed for greater understandings of population health. When conducting data intensive health research, engaging and involving the community is essential for conducting meaningful research that is responsive to the public’s needs. Particularly, when engaging Indigenous communities in research, there is a need to understand historical and ongoing impacts of colonialism and recognize the strengths in Indigenous Peoples’ knowledges and experiences while supporting Indigenous leadership and self-determination in research. This article describes the approach our research team/organization used to engage and involve Indigenous people living with HIV in three research projects using large, linked datasets and looking at HIV outcomes of Indigenous populations in Canada. The foundation of these projects was simultaneously: 1) supporting Indigenous people living with HIV to be involved as research team members, 2) developing research questions to answer with available datasets, and 3) integrating Indigenous and Western ways of knowing. We have identified important considerations and suggestions for engaging and involving Indigenous communities and individuals in the generation of research ideas and analysis of linked data using community-based participatory research approaches through our work. These include engaging stakeholders at the start of the project and involving them throughout the research process, honouring Indigenous ways of knowing, the land, and local protocols and traditions, prioritizing Indigenous voices, promoting co-learning and building capacity, and focusing on developing longitudinal relationships. We describe keys to success and learnings that emerged. Importantly, the methodology practiced and presented in this manuscript is not a qualitative study design whereby research subjects are surveyed about their experiences or beliefs. Rather, the study approach described herein is about engaging people with living experience to co-lead as researchers. Our approach supported Indigenous people to share research that addresses their research priorities and responds to issues relevant to Indigenous Peoples and communities.

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)0.022
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0160.031
Scholarly communication0.0270.018
Open science0.0040.019
Research integrity0.0060.017
Insufficient payload (model declined to judge)0.0380.018

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 head0.249
GPT teacher head0.548
Teacher spread0.299 · 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.

Study designTheoretical or conceptual
DomainMethods
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
Published2021
Admission routes2
Has abstractyes

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