Visualizing DEPICT: A Multistep Model for Participatory Analysis in Photovoice Research for Social Change
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".