Adaptation of a structured story-dialogue method for action research with social movement activists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dialogue and story-telling are essential elements of many qualitative methodologies and action research itself, reflecting the constructivist paradigm in which qualitative research (QR) and action research (AR) are grounded, and the co-construction of knowledge that takes place amongst research participants (in group settings) and researchers. This paper reports on the adaptation of a structured story-dialogue method for research with social movement activists undertaken in the form of a series of regional weekend workshops animated by researchers and attended by Transition movement leaders and participants from multiple locales, as part of a larger study ( www.transitionemergingstudy.ca ). We draw upon participant observation, animator reflections, research team meetings, participant feedback, as well as workshop materials, in relation to two different adaptations of Labonte and Feather’s original formulation (1996) and subsequent reflections (2011), setting this in the context of a broader literature on structured story-dialogue methods with groups. The potential of structured story-dialogue methods for research on, with and for social movements is highlighted.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.046 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it