Harm reduction in photovoice knowledge mobilization with 2S/LGBTQ+ youth who use(d) drugs: A community showcase
Bibliographic record
Abstract
Photovoice, a participatory photography approach, is an action-oriented research methodology that is increasingly being taken up in research with equity-owed populations and related to sensitive topics. This includes scholarship and activism with people who use(d) drugs, for which there are important ethical and pragmatic considerations. Among these is the challenge of drawing attention to substance use-related inequities yet not causing harm in (mis)representing communities, above all when sharing participant-produced photography with wider public audiences, including via photovoice exhibits. In this commentary, we draw from our recent photovoice research with 2S/LGBTQ+ youth who use(d) drugs to recount our process of planning and hosting a community photovoice exhibit, with the aim of highlighting implications for harm reduction-oriented and youth-engaged knowledge mobilization. We begin by introducing our Youth Action Committee, comprised of nine transgender, non-binary, and gender non-conforming youth who use(d) drugs, while briefly tracing our group's history of collaboration leading up the 'Queer Eyes, Queer Lives' photovoice exhibit, with Vancouver's 2024 Queer Arts Festival. Next, we discuss our participatory and intentional approach to planning this community event, including the steps we took to increase accessibility and turnout (e.g., venue selection, strategic partnerships) and make the exhibit youth-friendly and engaging (e.g., hiring a DJ, providing food, enlisting Youth Action Committee members as speakers). We then detail our approach to promoting youth choice in selecting generative and non-harmful photographs for the exhibit, such as by using photo-rating and photo-release processes during study data collection and deliberating with the Youth Action Committee to choose exhibit photographs that could most appropriately and respectfully represent research participant's multiple lived realities. We couch this practical discussion within a broader conversation about resisting damage- and deficit-focused narratives in substance use research, highlighting our group's active efforts to shift such narratives by showcasing photography reflecting community resiliencies, strengths, joys, and politicalities. The parts and sum of this commentary provide direction for other activist-scholars seeking to create, share, and mobilize art in substance use research and community intervention, and we end the paper by encouraging others to reinterpret and build on our approach through their own work.
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".