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Record W2152360129 · doi:10.18357/ijih31200612304

Community and Culture as Foundations for Resilience: Participatory Health Research with First Nations Student Filmmakers

2006· article· en· W2152360129 on OpenAlexaffvenueabout
Ted Riecken, Tish Scott, Michele Tanaka

Bibliographic record

VenueInternational Journal of Indigenous Health · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsParticipatory action researchSociologyCitizen journalismAction researchPlan (archaeology)Presentation (obstetrics)PhotovoiceParticipatory culturePsychological resilienceCommunity-based participatory researchPublic relationsAction planPedagogyMedia studiesPsychologyPolitical scienceEcologyGeographyEconomic growthMedicineSocial psychology

Abstract

fetched live from OpenAlex

This article describes a participatory action research project that brings together teachers and students from three First Nations education programs with researchers from the Centre for Youth & Society at the University of Victoria for the purpose of researching health and wellness among Aboriginal youth.Using the methodologies of participatory research, students identify topics that are of concern to them in the area of health and wellness.They plan, research and develop a video presentation on their chosen topic using digital video as a tool for research and communication of their findings.This article focuses on how such an approach to research contributes to building resiliency through the development of relationships that foster a connection with community and culture.The article describes the way the project has enhanced participants’ relationships with their communities, across generations, with diverse groups in urban settings, and with their sense of self, and culture.

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)0.036
metaresearch head score (Gemma)0.023
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 score0.036
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0160.018
Scholarly communication0.0080.005
Open science0.0020.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.664
GPT teacher head0.706
Teacher spread0.042 · 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

Citations19
Published2006
Admission routes3
Has abstractyes

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