The visual vernacular: embracing photographs in research
Bibliographic record
Abstract
The increasing use of digital images for communication and interaction in everyday life can give a new lease of life to photographs in research. In contexts where smartphones are ubiquitous and many people are "digital natives", asking participants to share and engage with photographs aligns with their everyday activities and norms more than textual or analogue approaches to data collection. Thus, it is time to consider fully the opportunities afforded by digital images and photographs for research purposes. This paper joins a long-standing conversation in the social science literature to move beyond the "linguistic imperialism" of text and embrace visual methodologies. Our aim is to explain the photograph as qualitative data and introduce different ways of using still images/photographs for qualitative research purposes in health professions education (HPE) research: photo-documentation, photo-elicitation and photovoice, as well as use of existing images. We discuss the strengths of photographs in research, particularly in participatory research inquiry. We consider ethical and philosophical challenges associated with photography research, specifically issues of power, informed consent, confidentiality, dignity, ambiguity and censorship. We outline approaches to analysing photographs. We propose some applications and opportunities for photographs in HPE, before concluding that using photographs opens up new vistas of research possibilities.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".