Framing Futurities in Photovoice, Health, and Environment: How Power Is Reproduced and Challenged in Arts-Based Methods
Bibliographic record
Abstract
Anchored in critical analysis of a photovoice project, this article interrogates intersections between (1) health as it is tethered to ideas about the “future” and (2) worries about “the environment.” The ways the concepts of future, health and environment are dealt with by project participants suggest that arts-based research methods may be at risk of being seen as non-political spaces safe for people with privilege to envision some peoples as having more rights than others to a healthy future. The article begins by exploring how arts-based approaches, and photovoice in particular, can result in positive generative conversations between differently positioned research collaborators. Then, guided by critical anti-racist, queer, and Indigenous scholarship on futurities and ecologies, we move on to suggest that arts-based methods might rightly be critiqued for appearing as naïve methods, susceptible to reinscribing dominant paradigms of power and privilege. This tension has implications for geohumanities, explored in the concluding sections of the article. Ultimately, we argue that working with arts-based methods across sectors must acknowledge and account for gradations of power. Gradations of power are, after all, always informing who is afforded and allowed a healthy future when what is broadly referred to as “the environment” is at stake.
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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 | 0.093 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.026 | 0.155 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".