Picturing a Home: A New Perspective on Home-Making Through Photo-Elicitation
Bibliographic record
Abstract
The photo-elicitation method can provide rich insights into home-making – the process whereby residents use, modify and personalize domestic space. However, previous studies have prioritized the words of participants, gathered in follow-up interviews, over photographs themselves. This paper presents an alternative methodological approach. Specifically, we approach photographs as primary sources, with a focus on their composition. First, we demonstrate how formal elements of photographs can be identified, and their meanings analysed. We show that domestic photographs speak to people’s relationships with their homes, and identify empirical insights into scale, light and absences. Second, we combine data from follow-up interviews with formal analysis of images to confirm these insights, and generate additional findings – e.g., regarding the negotiation of domestic architecture and the meanings of possessions. We conclude that photo-elicitation studies of home-making benefit from both visual and narrative insights into participants’ homes, and that both types of data merit serious analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".