Bibliographic record
Abstract
Abstract People with mental health disorders frequently come into contact with the criminal justice system (Borum et al., 1997; Ditton, 1999; Lamb and Weinberger, 1998; Teplin, 1988). In fact, encounters with police (the front line of the criminal justice system) are so common that for people with severe mental illness, they are the norm rather than the exception (Borum, 2000; Clark et al., 1999; Frankie et al., 2001; McFarland et al., 1989). Most of these contacts are precipitated by disruptive behavior or minor infractions that occur because individuals are experiencing psychiatric symptoms or social disruptions related to their disability. They frequently result in arrest, each year causing more than a quarter of a million people with mental illness to be processed through the criminal court system (Ditton, 1999). Many misdemeanants are held in jails; others are charged with more serious offenses and sent to prison. As of 1999, it is conservatively estimated that 123,000 people with severe mental illnesses were lodged in state prisons; at least 14,000 were in federal prison (Beck, 2000); and more than half a million were on probation (Ditton, 1999). Although some people with mental illness do commit offenses for which incarceration is the most appropriate disposition, many are confined as a result of arrests for minor infractions. This outcome is costly and poses a severe challenge to the criminal justice and behavioral health systems.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".