MétaCan
Menu
Back to cohort
Record W2753215335 · doi:10.15402/esj.v2i1.196

Pursuing Mutually Beneficial Research: Insights from the Poverty Action Research Project

2017· article· en· W2753215335 on OpenAlexfundvenueno aff
Jennifer S. Dockstator, Eabametoong First Nation, Misipawistik Cree First Nation, Opitciwan Atikamekw First Nation, Sipekne'katik First Nation, Lillooet BC T'it'q'et, Gérard Duhaime, Charlotte Loppie, David Newhouse, Frederic C. Wien, Wanda Wuttunee, Jeff Denis, Mark S. Dockstator

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsIndigenousAction researchReciprocity (cultural anthropology)PovertySociologyPacePsychological resilienceWork (physics)Action (physics)Political sciencePublic relationsEngineering ethicsEnvironmental ethicsSocial sciencePsychologySocial psychologyEngineeringPedagogyGeographyEcology

Abstract

fetched live from OpenAlex

Research with, in, and for First Nations communities is often carried out in a complex environment. Now in its fourth year, the Poverty Action Research Project (PARP) has learned first-hand the nature of some of these complexities and how to approach and work through various situations honouring the Indigenous research principles of respect, responsibility, reciprocity, and relevance (Kirkness & Barnhardt, 2001). By sharing stories from the field, this article explores the overarching theme of how the worlds of academe and First Nations communities differ, affecting the research project in terms of pace, pressures, capacity, and information technology. How PARP research teams have worked with these challenges, acknowledging the resilience and dedication of the First Nations that are a part of the project, provides insights for future researchers seeking to engage in work with Indigenous 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 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)0.918
metaresearch head score (Gemma)0.528
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9180.528
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.9620.003
Scholarly communication0.0110.003
Open science0.0040.000
Research integrity0.0010.747
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 head0.391
GPT teacher head0.504
Teacher spread0.114 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEngaged Scholar Journal Community-Engaged Research Teaching and LearningSame topicIndigenous Health, Education, and RightsFrench-language works237,207