“Just because they aren't human doesn't mean they aren't alive”: The methodological potential of photovoice to examine human-nature relations as a source of resilience and health among urban Indigenous youth
Bibliographic record
Abstract
Photovoice has been widely used as a participatory visual research methodology within the social sciences and health research. Given photovoice's critical and pedagogical potential, its advancement within Indigenous resilience and health research has been particularly prevalent. However, it has largely failed to problematize the concept of 'voice' to the extent of theorizing and engaging with the 'voices' of other kinds of life with consequences for theory and method. In this paper we re-examine the methodological potential and utility of photovoice methods to include other-than-human 'voices' during the empirical study of place-making, human-nature relations, and resilience and health. We analyze photo-narratives from a community-based, participatory research project involving Indigenous youth in Saskatoon, Saskatchewan in order to revisit 1) what we did to produce those images and 2) what we saw and heard in images. Our results suggest that when photovoice methods consider a relational and affective understanding of subjective reality during research practice, they have the capacity to capture and handle other-than-human 'voices'. Accordingly, we discuss future directions when adapting photovoice methods for the study of environmental repossession and dispossession within contested contexts of and encounters with methodological complexity, uncertainty, and emergence.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".