The eating disorder recovery assemblage: Collectively generating possibilities for eating disorder recovery
Bibliographic record
Abstract
In this paper, we explore the affective-discursive-material aspects of the supportive eating disorder recovery assemblage. We approach recovery as an “assemblage” to facilitate an understanding of how human (people, systems of care, etc.) and nonhuman (affect, discourses, etc.) forces generate possibilities or impossibilities for recovery. Moving away from framings of recovery as an individual achievement, we consider the relationality and dynamism of eating disorder recovery in interviews with 20 people in recovery and 14 supporters of people in recovery. We draw from experiential accounts to theorize a supportive eating disorder recovery assemblage in relation to trust and love mobilized in interactions and relationships. This supportive eating disorder recovery assemblage can scaffold new understandings of recoveries as multiple and co-produced. Supportive eating disorder recovery assemblages generate improvisational spaces, albeit loosely contained and bounded, for different pathways to and manifestations of “recoveries”. This work builds on a body of feminist scholarship on eating disorders/disordered eating that takes up gendered relationships of power in treatment settings, extending toward and analysing material, affective, embodied, and potentially affirming dimensions of care and emotion in participants’ lives.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".