Embodying Critical and Corporeal Methodology: Digital Storytelling With Young Women in Eating Disorder Recovery
Bibliographic record
Abstract
Digital storytelling is as an arts-based research method that offers researchers an opportunity to engage deeply with participants, speak back to dominant discourses, and re-imagine bodily possibilities. In this article, we describe the process of developing a research-based digital storytelling curriculum exploring eating disorder recovery. We have built this curriculum around research interviews with young women in recovery as well as research and popular literature on eating disorder recovery. Here, we highlight how the curriculum acted as a scaffolding device for the participants' artistic creation around their lived experiences of recovery. The participants' stories crystallize what resonated for them in the workshop process: they each have an open-ended narrative arc, emphasize the intercorporeality of recovery, and focus on recovery as process. The nuances within each story reveal unique embodied experiences that contextualize their recoveries. Using the example of eating disorder recovery, we offer an illustration of the possibilities of digital storytelling as a critical arts-based research method and what we gain from doing research differently in terms of participant-researcher relationships and the value of the arts in disrupting dominant discourses. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs160278
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".