Bibliographic record
Abstract
Gender-specific programming is viewed as essential to effective work with girls and women in the juvenile justice and criminal justice systems. Gender-specific programming can be defined as treatment that is especially designed to meet the needs of persons based on biological, psychological, and social needs unique to one’s gender. Regarding female offenders, the three strongest arguments for gender specific programming are women’s unique biology, cultural role expectations and vulnerabilities, and gendered pathways into crime. This entry identifies resources that pertain to working with girls and women who are in the criminal justice system, some confined in institutions, others participating in community correctional programs. There are few books and articles specific to social work in this specialized field, and most of the material derives from the criminal justice and feminist literature. Although there is extensive literature on recommended treatment for female offenders, the gap between theory and practice is large. Some of the literature in this entry derives from Canadian and British sources—a fact reflective of the research being done in progressive correctional counseling—and concerns treatment innovations in the system and writings on deficiencies in the treatment. That girls and women in trouble with the law have special needs is a major theme throughout all the literature. Helpful therapeutic strategies that are contained in the listings in this article are restorative justice, transcendental meditation, and strengths-based and trauma-informed practices. Because of the heavy involvement of girls and women with substance use and mental disorders in the criminal justice system, whole sections on these topics are included as well as a section on co-occurring disorders. Where possible, the selected readings contain material related to treatment interventions with female offenders.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.009 |
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".