Redefining Boundaries: A Grounded Theory Study of Recidivism in Women
Bibliographic record
Abstract
Incarceration rates for women in the United States are at least three times greater than those of any other nation (National Council on Crime and Delinquency, 2006 Sandelowski, M. and Barroso, J. 2002. Finding the findings in qualitative studies. Journal of Nursing Scholarship, 34: 213–219. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Recidivism, reoffense following prior conviction for a crime, compounds this problem, with nearly seven out of 10 convicted persons in the United States rearrested within 3 years of release (Hughes & Wilson, 2003 Hutchinson, S. A. and Wilson, H. 2001. “Understanding clients’ health care decisions: A grounded-theory approach”. In Qualitative methods for nursing research , 2nd ed., Edited by: Munhall, P. 245–276. Sudbury, MA: Jones and Bartlett Publishers. [Google Scholar]). Our purpose in this article is to present a grounded theory of recidivism from the perspective of incarcerated women and to discuss the public health and other societal implications. While the participants in this research were U.S. women, we hope to provide a new perspective on the decision making of some women that results in recidivism, in order to initiate dialogue that could lead to more effective interventions worldwide.
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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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".