Participant-driven photo-elicitation in library settings: A methodological discussion
Bibliographic record
Abstract
With the current attention in libraries on user-focused services and spaces, there is an increased interest in qualitative research methods that can provide insight into users’ experiences. In this paper, we advance photo-elicitation—a research method that employs photographs in interviews—as one such method. Although widely used in the social sciences, photo-elicitation has seen comparatively little uptake in Library and Information Studies (LIS). Here, we provide an overview of the method, consider epistemological and theoretical approaches, discuss cases of its application in library contexts and examine the benefits of using photo-elicitation for LIS research. We draw on our own research experiences and argue that photo-elicitation is a productive method for learning about the lived experiences of our users and for creating a collaborative approach to library research.
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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 | 0.383 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".