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Record W2759810772 · doi:10.15402/esj.v2i1.197

Effectively Engaging with Indigenous Communities through Multi-Methods Qualitative Data Collection and an Engaged Communications Plan

2017· article· en· W2759810772 on OpenAlexvenueaboutno aff
Lee Swanson, Joelena Leader, Dazawray Landrie-Parker

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPlan (archaeology)Public relationsQualitative researchData collectionSociologyPolitical scienceGeographySocial science

Abstract

fetched live from OpenAlex

A research project on social and economic capacity building through Aboriginal entrepreneurship employed a highly engaged approach with communities in northern Saskatchewan, Canada. The involved communities were viewed as research partners, and the research team applied a comprehensive communications plan to provide community members with relevant and timely information about the project and summaries of its outcomes as those results emerged. The study was designed to empower those who traditionally had been viewed as participants on whom research could be conducted, and ensure that the research was instead conducted with and for them. This research project encouraged youth and adults to express their perspectives in new and engaging ways that gave them the opportunity to more meaningfully have their voices heard. One important outcome from engaging more with communities was that research team members felt more engaged with their own project.

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)0.159
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.841

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1590.092
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0120.011
Scholarly communication0.0080.006
Open science0.0040.013
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.609
GPT teacher head0.608
Teacher spread0.000 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2017
Admission routes2
Has abstractyes

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