A Post-Structural Feminist Analysis of Eating Disorders Intervention Research
Bibliographic record
Abstract
Through an epistemological stance of post-structural feminism, this conceptual paper explores the use of language within eating disorders (ED) intervention articles, and the problematic narratives and power dynamics that are reinforced through this discourse. The paper begins with a vignette coupled with reflexive analysis of the authors’ experiences within a hospital-based ED unit. The authors then engage in a post-structural feminist analysis to discuss how language within ED intervention research relay problematic narratives of: (1) the individual with an ED as passively, not actively, engaged in care; (2) that their experiences can be captured and categorized; and (3) that measurement based scientific knowledge is more valuable than the lived experiences of clients. Overall, the authors argue that these narratives not only shape how social work researchers think of EDs, but also what we think of those with EDs. These themes also signal a larger power dynamic that continuously favours the epistemic value of researchers’ knowledge over that of the client’s, which runs contrary to the guiding principles of client-centered care in social work. To address these critiques, the authors recommend that social work researchers adopt an eco-social phenomenological approach informed by post-structural feminism when conducting ED intervention research.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".