Exploring the Women's Incarceration Problem
Bibliographic record
Abstract
The United States leads the world in incarceration rates, accounting for a quarter of the world’s incarcerated population. As efforts to improve the existing mass incarceration problem have led to decreases in male prison populations, women’s incarceration rates have been outpacing men, and they are now the fastest growing segment. Although they make up a smaller percentage of the overall incarcerated population, females report the highest rates of current and past mental health problems at more than two times their male counterparts. Extensive psychological research shows that women and men enter the prison system through different pathways and with very different histories. Even though women are exposed to higher risk factors of mental illness and substance abuse, almost all of the United States’ criminal justice policy is gender neutral or crafted based on research of male participants or for the correction of male inmates. As a result of the lack of relevant resources specific to women, current corrections systems are unable to adequately meet the psychological needs specific to female prisoners. In order to address the health crisis among incarcerated women, there is a need for gender- responsive and trauma- informed care to acknowledge and account for the crucially different life experiences women and men experience. This paper examines the current state of mental health in female offenders, differences in legal standards and histories leading to incarceration, and recommendations for gender- aware policy reform and treatment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".