Draw me a picture, tell me a story: Evoking memory and supporting analysis through pre-interview drawing activities
Bibliographic record
Abstract
In interviews for interpretive inquiry or interpretive case studies, researchers hope to grasp participants’ perspectives and learn about the nature and meaning of their experiences. There are many challenges or requirements for useful or successful interviews. In this paper we identify important aspects of interviews and examine the helpful contributions of using pre-interview activities. Pre-interview activities were drawings or diagrams that participants completed about the experiences of interest. Participants brought the completed drawings to their interviews and the interviews commenced with presentation and discussion of these visuals. This paper presents four studies that illustrate how the use of pre-interview activities can support participants in identifying central ideas in their experiences. In the interviews, the participants spoke at length about the visual representations they produced and in these reflections they identified central ideas or key themes in the experiences. Some drawings were a source of visual metaphors for discussing the experience and some highlighted whole-part relationships that informed interpretation. The findings contribute to conversations about how to “invite stories” rather than “request reports” from participants, how images other than photographs can serve as evocative and potent visuals to support memory and reflection in interviews, and how researchers can better or more directly access a participant’s meaning.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".