Best Practices for Research: Conducting Research with Criminalized Women in an Incarcerated Setting: The Researcher's Perspective
Bibliographic record
Abstract
Although women incarcerated by the criminal justice system encounter significant challenges to their health, there has been little research focusing on their health practices. To contribute to the research literature on the health experiences of criminalized women, the authors conducted a multi-method study as part of a program of research exploring the health promotion and health-literacy skills of women in conflict with the law. Conducting research in an incarcerated setting posed unique challenges and ethical dilemmas that problematized each phase of data collection. The authors share their experiences as health researchers conducting research in an incarcerated setting and with criminalized women. They document some of the challenges, successes, and valuable lessons learned during the research process in the hope that by sharing their knowledge with other health researchers they will support future studies with criminalized women.
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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 | 0.300 | 0.356 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.030 | 0.071 |
| Scholarly communication | 0.037 | 0.021 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.028 | 0.041 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".