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Record W2892138780 · doi:10.23889/ijpds.v3i4.999

Perspectives on Linkage Involving Indigenous data

2018· article· en· W2892138780 on OpenAlexaffabout
Jennifer Walker, Bonnie Healy, Chyloe Healy, Tina Apsassin, William E. Wadsworth, Carmen Jones, Jeff Reading, Laurel Lemchuk-Favel, Raymond Lovett, Donna Cormack, Desi Rodriguez-Lonebear, Robyn Rowe

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsUniversity nuhelot'ine thaiyots'i nistameyimâkanak Blue QuillsProvidence Health CareLaurentian University
Fundersnot available
KeywordsIndigenousSovereigntyPopulationMetisPolitical scienceSociologyGeographyPublic relationsLawPoliticsDatabase

Abstract

fetched live from OpenAlex

Topic: Perspectives on Linkage Involving Indigenous dataIndigenous populations across the globe are reaffirming their sovereignty rights in the collection and use of Indigenous data. The Indigenous data sovereignty movement has been widely influential and can be unsettling for those who routinely use population-level linked data that include Indigenous identifiers. Ethical policies that stipulate community engagement for access, interpretation and dissemination of Indigenous data create an enabling environment through the critical process of negotiating and navigating data access in partnership with communities. This session will be designed to create space for leading Indigenous voices to set the tone for the discussion around Indigenous population data linkage. Objectives: To provide participants with an opportunity to build on the themes of Indigenous Data Sovereignty presented in the keynote session as they apply to diverse Indigenous populations. To explore approaches to the linkage of Indigenous-identified population data across four countries, including First Nations in three Canadian regions. To share practical applications of Indigenous data sovereignty on data linkage and analysis and discussion. To center Indigenous-driven data linkage and research. Facilitator:Jennifer Walker. Canada Research Chair in Indigenous Health, Laurentian University and Indigenous Lead, Institute for Clinical Evaluative Sciences. Collaborators: Alberta: Bonnie Healy, Tina Apsassin, Chyloe Healy and William Wadsworth (Alberta First Nations Information Governance Centre) Ontario: Carmen R. Jones (Chiefs of Ontario) and Jennifer Walker (Institute for Clinical Evaluative Sciences) British Columbia: Jeff Reading (Providence Health Centre) and Laurel Lemchuk-Favel (First Nations Health Authority) Australia: Raymond Lovett (Australian National University) Aotearoa / New Zealand: Donna Cormack (University of Otago) United States: Stephanie Rainie and Desi Rodriguez-Lonebear (University of Arizona) Session format: 90 minutesCollaborators will participate in a round-table introduction to the work they are doing. Collaborators will discuss the principles underlying their approaches to Indigenous data linkage as well as practical and concrete solutions to challenges. Questions to guide the discussion will be pre-determined by consensus among the collaborators and the themes will include: data governance, community engagement, Indigenous-led linkage and analysis of data, and decision-making regarding access to linked data. Other participants attending the session will be encouraged to listen and will have an opportunity to engage in the discussion and ask questions. Intended output or outcome:The key outcome of the session will be twofold. First, those actively working with Indigenous linked data will have an opportunity for an in-depth and meaningful dialogue about their work, which will promote international collaboration and sharing of ideas. Second, those with less experience and knowledge of the principles of Indigenous data sovereignty and their practical application will have an opportunity to listen to Indigenous people who are advancing the integration of Indigenous ways of knowing into data linkage and analysis. The output of the session will be a summary paper highlighting both the diversity and commonalities of approaches to Indigenous data linkage internationally. Areas where consensus exists, opportunities for collaboration, and challenges will be highlighted.

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)0.253
metaresearch head score (Gemma)0.174
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.253
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2530.174
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.012
Science and technology studies0.0600.066
Scholarly communication0.0380.054
Open science0.0080.047
Research integrity0.0230.031
Insufficient payload (model declined to judge)0.0120.001

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 head0.465
GPT teacher head0.558
Teacher spread0.093 · 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 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
Published2018
Admission routes2
Has abstractyes

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