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Record W2947991891 · doi:10.15402/esj.v5i2.68342

Unpacking the Layers of Community Engagement, Participation, and Knowledge Co-Creation when Representing the Visual Voices of LGBTQ Former Foster Youth

2019· article· en· W2947991891 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
FundersUniversity at AlbanyCalifornia State University, Northridge
KeywordsPhotovoiceUnpackingParticipatory action researchVisual researchCitizen journalismSociologyProcess (computing)Visual methodsOrder (exchange)Community-based participatory researchPublic relationsPsychologyVisual artsComputer sciencePolitical scienceWorld Wide WebCognitive scienceAnthropology

Abstract

fetched live from OpenAlex

This article highlights the various ways in which we represented the visual voices of LGBTQ former foster youth through photovoice methodology in order to engage various stakeholders, diverse communities, and the participants themselves. We locate our research within other similar community-based, participatory projects and weave in our collective experiences. Through the juxtaposition of academic literature with the various steps of our research process, this article provides our critical reflections of our engagement process as we prepared for the research, interacted with the community, shared our findings, and incorporated social change efforts through the dissemination of the visual data in various formal and informal spaces.

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.

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)0.893
metaresearch head score (Gemma)0.623
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, 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 score0.518
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8930.623
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.3110.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.348
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 head0.607
GPT teacher head0.614
Teacher spread0.007 · 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