Exposing the Expert Discourse in Psychiatry: A Critical Analysis of an Anti-Stigma/Mental Illness Awareness Campaign
Bibliographic record
Abstract
Drawing on a situational analysis of a recent anti-stigma campaign in psychiatry (Defeat Denial: Help Defeat \nMental Illness) this paper seeks to engage with the reader on the use of an expert discourse that focuses on \nthe brain and its disruption as a way to address stigma associated with mental illness. To begin, we briefly \nhighlight key statistics regarding the impact of mental illness in Canada and introduce the concept of stigma. \nWe then introduce the Defeat Denial media campaign and describe the analytical process employed for \nthis paper - Situational Analysis with a specific focus on discourse. We then expand on the use of the expert \ndiscourse in the awareness campaign by making connections with Rose’s concept of biological citizen and, \nin the final sections, present recent studies on stigma that highlight the paradox and contested construction \nof the (bio)psychiatric self
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.030 | 0.051 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| 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".