“It’s Not Just a Picture When Lives are at Stake: Ethical Considerations and Photovoice Methods with Indigenous Peoples Engaged in Street Lifestyles”.
Bibliographic record
Abstract
Photovoice is an arts-based, participatory research method in which participants take photographs to document their understanding of the research question. It engages participants in a process of creating and sharing photographs and dialogue, supports connections with others and can be a key tool for policy change advocacy. This method has grown in popularity over the years and has been heralded as ideal for research with Indigenous communities and other marginalized populations. While photovoice offers clear benefits, little research has considered the ethical dilemmas that can arise from this method from an Indigenous specific lens. This paper describes the photovoice approach and its benefits, notably its engagement and empowerment aspects. We then explore the ethical challenges photovoice raises drawing on a recent study that investigates the ways in which Indigenous men engage in street lifestyles. We conclude by offering lessons learned to guide the work of researchers using photovoice with Indigenous peoples or other marginalized populations.
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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 | 0.101 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.051 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".