The Power of a Camera: Fieldwork Experiences From Using Participatory Photovoice
Bibliographic record
Abstract
Conducting primary data collection can be a fulfilling and interesting adventure producing significant learning experiences particularly for early career researchers. However, fieldwork can be marred with complex challenges and frustrations, especially if conducted in dynamic and politically sensitive environments and with highly vulnerable urban populations. This paper contributes to and advances academic scholarship on fieldwork experiences in the social sciences. Drawing from the first author’s doctoral fieldwork experiences, we share our reflections on the application of the photovoice method in researching street traders in Harare, Zimbabwe. We engage with different issues that researchers could consider in the application of photovoice, especially with dynamic and marginalized urban populations like street traders. These include dealing with and managing complex and multiple ethical dilemmas, dealing with the content-quality conundrum, exploring ‘missing’ photographs and handling ‘leftover’ photographs, handling conflictual council-street trader relations, building rapport, and ensuring participant commitment, joint interpretation, and co-construction of meaning and methodological benefits of using photovoice with street traders. To the best of our knowledge, this is the first paper that reflects on the use of photovoice with street traders in Global South cities, and we hope that the insights presented here will be useful for future urban researchers working on similar topics.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".