Marginality and psychiatry – two intersecting worlds: psychiatrist- and lived-experience perspectives
Bibliographic record
Abstract
Against the background of strong social determinants and modulators on the incidence, prevalence, course and lived experience of the dominant western conceptualisation of 'mental illness', this text explores literature in the fields of subaltern/subaltern studies, precarity/precariousness, the relationship between precarity and mental health outcomes, as well as madness/Mad Studies. These fields of work offer key, under-explored insights into matters of importance for the practice of psychiatry at the clinical coalface and across society. In the discussion, the authors debate, from survivor/lived-experience and professional perspectives, whether a combination of autonomous, collaborating (and potentially competing and conflicting) (i) user-controlled organisations of mutual help, support, Mad Studies (as part of and in alliance with New Social Movements (NSMs)), and (ii) professionally-controlled systems of practice and research in the fields of psychiatry and mental health could work effectively together but also in some tension with each other. Engaging in dialogue and debate on these issues could help psychiatry and our broader understanding of madness and mental distress move forward.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".