Against the Medicalization of Humanity: A Critical Ethnography of a Community Trying to Build a World Free of Sanism and Psychiatric Oppression
Bibliographic record
Abstract
We have to stop inventing disorders for every human experience that challenges the status quo… I dream of a world where people can peacefully co-exist… [where] differences are accepted… [and where] I, and everybody else, has a place (Jackie, psychiatric survivor and mad person). The thesis is a critical ethnography of a political community in Toronto, Canada whose members are challenging the theories and interventions of biological psychiatry and developing approaches to understanding and responding to human experience in alternative ways that empower people who are conceived of as “mad”. Based on the emerging ideological and practical differences among participants, a model of the community was developed that includes three main constituencies: the psychiatric survivor constituency, the mad constituency, and the antipsychiatry constituency. This thesis includes descriptive accounts of the philosophical understandings, priorities, goals, actions, and strategies emerging from each of these constituencies; some tensions and conflicts that arise in the community around working across difference; the genuine attempts made by community members to build alliances, the challenges they face, and the notable progress they have made. The thesis grapples with how community members might work towards building a paradigm for solidarity work with others who share a stake in building communities that are free of sanism and psychiatric oppression. The dissertation ends with an exploration of how clinical and counselling psychologists might proceed in their work taking into consideration the experiences and perspectives shared by participants.
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 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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.045 | 0.056 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".