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Record W2984712445 · doi:10.1093/geroni/igz038.2078

COMMUNITY-BASED PARTICIPATORY RESEARCH FILMMAKING WITH FORMERLY HOMELESS OLDER ADULTS

2019· article· en· W2984712445 on OpenAlexaff
Victoria Burns

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFilmmakingParticipatory action researchCitizen journalismOpenness to experienceCommunity-based participatory researchSociologyNegotiationInterpersonal communicationMedia studiesGender studiesPublic relationsPsychologySocial psychologyPolitical scienceVisual artsSocial scienceArtAnthropology

Abstract

fetched live from OpenAlex

Abstract This methodological paper discusses the process of co-creating a documentary film with seven formerly homeless older adults, highlighting some of the tensions carrying out community-based participatory research (CBPR). This paper is part of a larger study that explored ‘finding home’ through a series of individual and group audio and video-recorded interviews (including walk and drive alongs) with seven adults (aged 50+) with diverse homeless histories. In addition to the main findings, participants shared their experience of filmmaking and CBPR. Findings revealed four main tensions: 1) openness of sharing stories versus privacy and anonymity; 2) balancing participation/engagement and over-burdening; 3) negotiating interpersonal conflict and community building; and 4) ethical issues surrounding copyright and ownership of the film. Ultimately, we advocate for more CBPR film projects, as they not only provide a rich contextualized window into people’s everyday lives but serve to advance the voices of marginalized populations beyond traditional academic circles.

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)0.038
metaresearch head score (Gemma)0.038
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 score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.009
Scholarly communication0.0060.004
Open science0.0030.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.493
GPT teacher head0.520
Teacher spread0.027 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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