Photovoice as a Visual Research Method: Adaptations from Projects in Peru and Ecuador
Bibliographic record
Abstract
Photovoice is a visual research method which involves participants taking their own photos of a specific topic to represent their views. Projects using photovoice often follow a standard format, yet this does not always provide a good match with specific research situations. Based on experiences from two projects, studies of tourism in Peru and of media use by indigenous organizations in Ecuador, we outline specific modifications to the standard photovoice format that allowed us to better accommodate local cultural context and research needs. These adaptations include a reconsideration of group-focussed versus individual format, research design that fosters different ways of building rapport between participants and with the researcher, and critical reflections on the issue of empowerment. The final discussion considers a few of the complex representational issues associated with photovoice. First, the way that photovoice must be evaluated in light of the increasing prevalence of photography in daily life, with sharing through social media and cameras available on smart phones. The level of experience participants have with photography has an impact on the ways that photos are taken and shared. Photography is a practice deeply entwined with individuals’ understandings of aesthetics and sensory memories. When used with greater flexibility, the photovoice method can be better aligned with local realities and provide a creative and beneficial addition to the research tool kit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".