Experiences Labelled Psychotic: A Settlerâs Autoethnography beyond Psychosic Narrative
Bibliographic record
Abstract
This autoethnography uses narrative inquiry within an anticolonial theoretical framework. As a White Italian male settler living on Turtle Island, I bring survivor experience to psychiatric definitions of “psychosis,” or what I call psychosic narrative, and to broader literatures for the purpose of decolonizing “mental” relations. Using reflexive critiques, including feminist antiracism, I question my own privileges as I consider the possibilities of Mad culture to disturb authorizations of practices like forced electroshock and drugging. Using journals, salient themes of experience are identified, including “delusion,” “psychosis,” “madness,” and “illness,” especially as they appear in texts about politics, culture, and theory. A temporally rigorous narrative approach to my readings allows for a self-reflexive writing on such themes in relation with antiracist anticolonial resistance. Thus a White psychiatric survivor resistance to psychiatry and its social (local) history is related to the problematic of global Western neoliberal heteropatriarchy in psychological institutional texts. Survivor testimonies bring critical madness and disability theories as they pertain to racialization and constructions of sex/uality and gender. Rather than present a comprehensive analysis, this narrative inquiry is generated from the process of research as it was experienced in order to represent and question its epistemological grounds.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".