Bibliographic record
Abstract
People who have experienced eating disorders are making sense of and managing their own health and recoveries, in part by engaging with digital technologies. We analyzed 1056 images related to eating disorder recovery posted to Instagram using the hashtags #EDRecovery, #EatingDisorderRecovery, #AnorexiaRecovery, #BulimiaRecovery and #RecoveryWarrior to explore user performances of eating disorder recovery. We situated our analysis in a critical Deleuzian feminist frame, seeking to understand better how users represented, negotiated, or contested dominant constructions of “how to be recovered”. We identified a number of themes: A Feast for the Eyes, Bodies of Proof, Quotable, and (Im)Perfection. Within each of these themes, we observed links to social location, including the White, Western, middle-to-upper-class trappings that tether representations of eating disorder recovery to stereotypes about who gets eating disorders and may restrict access to the category of recovered. Documenting recovery online may be a way for those in recovery to chart progress and interact with similar others. However, recoveries presented on Instagram resemble stereotypical perspectives on who gets eating disorders and, thus, who might recover, subtly reinforcing a dominant recovery biopedagogy. These versions of recovery may not be available to all, limiting the possibility of engagement for people enacting and embodying diverse recoveries. Still, users make representational interventions into Instagram by making the struggles and challenges of eating disorder recovery visible to each other and to broader audiences.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".