U.S. v. My Mommy: Evaluation of Prison Nurseries as a Solution for Children of Incarcerated Women
Bibliographic record
Abstract
Several millions of children around the world suffer from the detrimental effects of parental incarceration. In the United States alone, over a quarter of a million children are separated from their mothers due to incarceration. Despite the fast growing magnitude of the problem and its vast effect on children, families and communities in the U.S. and around the world, relatively little attention is attributed to it in legal and social science scholarship. The article provides a comprehensive analysis of Prison Nursery Programs as a possible solution for children of incarcerated mothers. This is the first scholarly article to provide a diverse perspective that takes into consideration the rights and interests of all the parties involved, namely, the child, the mother, the state and the general public. It also provides a comparative analysis, suggesting policy improvements based on lessons learned from the experience of European countries in the field. Thus, the article provides a comprehensive basis for policy decisions concerning the institution of Prison Nursery Programs, as well as solutions for children suffering from parental incarceration in general. It also proposes new research directions that could advance this underexplored field.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".