To prevent arrest and convictions, prescribe antipsychotics
Bibliographic record
Abstract
Recently, this journal published an article by Sariaslan, Leucht, Zetterqvist, Lichtenstein, and Fazel (2021), which describes a cohort of individuals prescribed antipsychotics in Sweden between 2005-2013. The authors provide a methodologically rigorous study with a large sample (n = 74 925) from several national databases that control for time-invariant confounders (e.g., demographic characteristics). They conclude that periods of absence of antipsychotic prescription among individuals with psychotic disorders are associated with higher rates of arrests and convictions (Sariaslan et al., 2021). We applaud Sariaslan et al. for their clever study design but have several concerns and questions about the motives and message of the paper. We found that they did not sufficiently cover the complex background of literature on antipsychotic prescription, which weakens their supposition of covering time-invariant factors. Their broadly defined constructs, such as “crime rates,” also seemed misleading. They furthermore may stigmatize mental illness by promoting antipsychotics as a criminogenic intervention. [...]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".