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Record W3112222062 · doi:10.23889/ijpds.v5i5.1626

Planting the S.E.E.D.S of Indigenous Population Health Data Linkage

2020· article· en· W3112222062 on OpenAlexaff
Robyn Rowe, Jennifer Walker

Bibliographic record

VenueInternational Journal for Population Data Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsLaurentian University
Fundersnot available
KeywordsIndigenousSovereigntyLinkage (software)Linked dataPopulationData governanceSociologyPolitical sciencePublic relationsLawBusinessWorld Wide WebService (business)Data qualityComputer scienceEcologyPoliticsGenetics

Abstract

fetched live from OpenAlex

IntroductionThe increasing accessibility of data through digitization and linkage has resulted in Indigenous and allied individuals, scholars, practitioners, and data users recognizing a need to advance ways that assert Indigenous sovereignty and governance within data environments. Advances are being talked about around the world for how Indigenous data is collected, used, stored, shared, linked, and analysed.
 Objectives and ApproachDuring the International Population Data Linkage Network Conference in September of 2018, two sessions were hosted and led by international collaborators that focused on regional Indigenous health data linkage. Notes, discussions, and artistic contributions gathered from the conference led to collaborative efforts to highlight the common approaches to Indigenous data linkage, as discussed internationally. This presentation will share the braided culmination of these discussions and offer S.E.E.D.S as a set of guiding Indigenous data linkage principles.
 ResultsS.E.E.D.S emerges as a living and expanding set of guiding principles that: 1) prioritizes Indigenous Peoples’ right to Self-determination; 2) makes space for Indigenous Peoples to Exercise sovereignty; 3) adheres to Ethical protocols; 4) acknowledges and respects Data stewardship and governance, and; 5) works to Support reconciliation between Indigenous Peoples and settler states. S.E.E.D.S aims to centre and advance Indigenous-driven population data linkage and research while weaving together common global approaches to Indigenous data linkage.
 Conclusion / ImplicationsEach of the five elements of S.E.E.D.S interweave and need to be enacted together to create a positive Indigenous data linkage environment. When implemented together, the primary goals of the S.E.E.D.S Principles is to guide positive Indigenous population health data linkage in an effort to create more meaningful research approaches through improved Indigenous-based research processes. The implementation of these principles can, in turn, lead to better measurements of health progress that are critical to enhancing health care policy and improving health and wellness outcomes for Indigenous populations.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0040.002
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 head0.123
GPT teacher head0.398
Teacher spread0.275 · 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 designObservational
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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