Self-concept through the diagnostic looking glass: Narratives and mental disorder
Bibliographic record
Abstract
This paper explores how the diagnosis of mental disorder may affect the diagnosed subject's self-concept by supplying an account that emphasizes the influence of autobiographical and social narratives on self-understanding. It focuses primarily on the diagnoses made according to the criteria provided by the Diagnostic Statistical Manual of Mental Disorders (DSM), and suggests that the DSM diagnosis may function as a source of narrative that affects the subject's self-concept. Engaging in this analysis by appealing to autobiographies and memoirs written by people diagnosed with mental disorder, the paper concludes that a DSM diagnosis is a double-edged sword for self-concept. On the one hand, it sets the subject's experience in an established classificatory system which can facilitate self-understanding by providing insight into the subject's condition and guiding her personal growth, as well as treatment and recovery. In this sense, the DSM diagnosis may have positive repercussions on self-development. On the other hand, however, given the DSM's symptom-based approach and its adoption of the Biomedical Disease model, a diagnosis may force the subject to make sense of her condition divorced from other elements in her life that may be affecting her mental-health. It may lead her to frame her experience only as an irreversible imbalance. This form of self-understanding may set limits on the subject's hopes of recovery and may create impediments to her flourishing.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".