Bringing Socio-Narratology and Visual Methods to Focus Group Research
Bibliographic record
Abstract
Informed by narrative inquiry, this chapter makes a unique contribution to the theorizing of focus groups. It uses empirical data from research into perceptions of physically active older adults across the life course to critically examine the work that stories can do within a focus group setting. According to Frank (2010), the work of stories is to animate human life by working with people, for people, and always on people. Conceptualizing stories as active social interactions, which are heard and responded to, calls for a new way to collect, share, think about and study them. This approach, which Frank terms socio-narratology , aims to understand what the story does, rather than understand the story as a portal into the mind of the storyteller. Our chapter reports on the analysis of group meetings, which were undertaken with a total of twelve naturally occurring groups representing different stages of the life course. The focus group meetings involved sharing a range of visual material (photography and film), which represented the lived experiences of physical activity amongst a group of older adults. The stories conveyed through these visual stimuli worked with, for, and on the focus group participants, eliciting a range of responses that were imbued with stereotypes, contradictions and episodes of reflexivity. The value of adopting a socio-narratology approach to theorize focus groups, along with the innovative use of various visual data to examine ‘narratives at work’ is discussed.
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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 | 0.043 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".