Pregnancy in prison partnership – International (PiPPi): Building a collaborative global network of best practice for and with women prisoners
Bibliographic record
Abstract
It is well understood that the impact of being pregnant in prison has risks to the safety and wellbeing of women and their unborn babies. Pregnancy in Prison Partnership – International (PIPPI) is a collaboration of leading Midwifery and Health academic experts in Australia, the UK, the USA, New Zealand, and Canada who work with / are undertaking research into the health and wellbeing of pregnant women and new mothers in prison. We are a non-hierarchical collaboration of midwifery, medical, psychological, and health practitioners/academics, We are committed to the group as a collaboration with a focus on togetherness and a purpose to be a global network to build best practices for and with women prisoners. Our presentation will focus on how and why we brought our collaboration together, our current work, and our goals for the future. We will share our philosophy of working together for the greater good of the health and wellbeing of pregnant women and new mothers in prison worldwide. The impact of our work together will translate to highlighting the known health impacts, inconsistencies, and challenges of delivering midwifery care within patriarchal carceral institutions designed for punishment rather than health. We meet monthly via Zoom to explore collaborative grant opportunities, write together and share experiences of our different prison systems. We have prepared one manuscript to date which is currently under review. Our future plans include multi-country research to glean a worldwide perspective with recommendations to improve the health of perinatal women in prison.
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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 | 0.079 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".