Visualizing DEPICT: A Multistep Model for Participatory Analysis in Photovoice Research for Social Change
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.
Bibliographic record
Abstract
As a critical narrative intervention, photovoice invites community members to use photography to identify, document, and discuss issues in their communities. The method is often employed with projects that have a social change mandate. Photovoice may help participants express issues that are difficult to articulate, create tangible and meaningful research products for communities, and increase feelings of ownership. Despite being hailed as a promising participatory method, models for how to integrate diverse stakeholders feasibly, collaboratively, and rigorously into the analytic process are rare. The DEPICT model, originally developed to collaboratively analyze textual data, enhances rigor by including multiple stakeholders in the analysis process. We share lessons learned from Picturing Participation, a photovoice project exploring engagement in the HIV sector, to describe how we adapted DEPICT to collaboratively analyze participant-generated images and narratives across multiple sites. We highlight the following stages: dynamic reading, engaged codebook development, participatory coding, inclusive reviewing and summarizing of categories, and collaborative analysis and translation, and we discuss how participatory analysis is compatible with creative, interactive dissemination outputs such as exhibitions, presentations, and workshops. The benefits of Visualizing DEPICT include feelings of increased ownership by community researchers and participants, enhanced rigor, and sophisticated knowledge translation approaches that honor multiple forms of knowing and community leadership. The potential challenges include navigating team capacity and resources, transparency and confidentiality, power dynamics, data overload, and streamlining "messy" analytic processes without losing complexity or involvement. Throughout, we offer recommendations for designing participatory visual analysis processes that are connected to critical narrative intervention and social change aims.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it