Reflecting on the Delivery of the Inside-Out Prison Exchange Program During the COVID-19 Pandemic
Bibliographic record
Abstract
The Inside-Out Prison Exchange Program (IOPEP) encompasses university (outside) students and incarcerated (inside) students undertaking a university course alongside each other behind the walls of a prison.In the Australian IOPEP, students are taught Comparative Criminal Justice Systems.In 2020, the IOPEP was moved online halfway through the course delivery due to the COVID-19 pandemic to reduce the potential of transmission in prisons.In 2021, in adherence to COVID-19 safety regulations and restrictions in prisons, the IOPEP delivery was also modifi ed by reducing the number of outside students coming into prison.This paper presents Haozhou Sun's refl ection of his 2021 IOPEP's learning experience.Although Sun was not able to have the same level of interaction traditionally obtained by IOPEP students, it is clear that the majority of the program's aims were still achieved despite changes in delivery. WHAT IS THE IOPEP?The Inside-Out Prison Exchange Program (IOPEP) "is a blended learning program where university (outside) students and incarcerated (inside) students come together as equals in the university context to learn with and from each other, in prison, whilst undertaking a university subject" (Van Grundy et al., 2013 as cited in Martinovic et al., 2018, p. 437).The program was implemented by Dr. Marietta Martinovic from the Royal Melbourne Institute of Technology (RMIT) University in collaboration with Corrections Victoria (Department of Justice and Community Safety) (Martinovic et al., 2018).IOPEP is currently being delivered across six Victorian prisons.IOPEP was developed in 1997 by Lori Pompa of Temple University and a lifer named Paul Perry at Graterford prison (King et al., 2019).It was subsequently expanded into a training package to enable other higher education providers to facilitate the program (IOPEP Centre, 2017).Up until 2022, over 2000 IOPEP classes have been taught, with more than 60,000 students participating in classes across the USA, Canada, Australia, United Kingdom, and Norway (Inside-Out Centre, 2022).
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.012 |
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".