“I Just Don't Think There's any other Image that Tells the Story like [This] Picture Does”: Researcher and Participant Reflections on the Use of Participant-Employed Photography in Social Research
Bibliographic record
Abstract
The incorporation of visual forms of expression has become common in qualitative research over the past two decades, with participant-employed photography being most prevalent. Visual methods such as photovoice have been used in community-based studies and with individuals to explore their lived experiences, particularly because of their participatory nature. Despite widespread support for visual approaches in existing research, there has been insufficient attention paid to how photography can enhance understanding of the phenomenon under study. Additionally, the existing literature is somewhat bereft of discussion of what individuals think about their participation in studies that incorporate participant-employed photography, or researchers' perspectives of carrying out this type of research. In this article, we describe a photovoice study carried out with young adult women affected by serious illness and provide examples of participants' photographs to illustrate how participant-employed photography can enhance the depth of research data. Specifically, the examples highlight how the photographs enriched participants' verbal descriptions of their lived experiences, which generated a better understanding of their personal embodied realities. We also discuss the young adult women's inclusion of previously taken photographs and reflections on their participation in the study. Finally, we examine the need to consider the intended audience of photographs, and specific ethical and methodological considerations for researchers contemplating the incorporation of participant-employed photography. In doing so, we provide insight into the advantages and challenges of photo-methods, which can inform other researchers contemplating the incorporation of participant-employed photography into social research.
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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".