Picturing femininities and masculinities: Using visual methods to explore gender relations
Bibliographic record
Abstract
Arts-informed and arts-based methods are becoming more visible in qualitative psychological research. This study demonstrates how the use of visual images, through two visual methods photo-elicitation (PE) and photovoice (PV), can act as an innovative research tool for researchers. This paper focuses on the impacts of the visual images on the research process. A systematic search strategy was used to search 10 health and social science databases, with 2478 relevant articles retrieved, 197 articles were identified for review, and 75 articles met inclusion criteria. Qualitative synthesis and thematic analysis were selected to provide a flexible framework for addressing the research questions. The findings demonstrate the value of visual images being their ability to materialize bodies, social practices, and interactions between individuals and structures. These findings show that these two visual methods present researchers with an innovative process that can generate new insights and perspectives that are useful for studying aspects of gender relations (e.g., femininities and masculinities) and other aspects of social experience.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".