Structural Stigma in State Legislation
Bibliographic record
Abstract
OBJECTIVE: This article discusses examples of structural stigma that results from state governments' enactment of laws that diminish the opportunities of people with mental illness. METHODS: To examine current trends in structural stigma, the authors identified and coded all relevant bills introduced in 2002 in the 50 states. Bills were categorized in terms of their effect on liberties, protection from discrimination, and privacy. The terms used to describe the targets of bills were examined: persons with "mental illness" or persons who are "incompetent" or "disabled" because of mental illness. RESULTS: About one-quarter of the state bills reviewed for this survey related to protection from discrimination. Within that category, half the bills reduced protections for the targeted individuals, such as restriction of firearms for people with current or past mental illness and reduced parental rights among persons with a history of mental illness. Half the bills seemed to expand protections, such as those that required mental health funding at the same levels provided for other medical conditions and those that disallowed use of mental health status in child custody cases. Legislation frequently confuses "incompetence" with "mental illness." CONCLUSIONS: Examples of structural stigma uncovered by surveys such as this one can inform advocates for persons with mental illness as to where an individual state stands in relation to the number of bills that affect persons with mental illness and whether these bills expand or contract the liberties of this stigmatized group.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".