Student perspectives on creating a positive classroom dynamic: science education in prison
Bibliographic record
Abstract
Detailed student perspectives on their involvement in prison education are limited in published literature, yet such contributions are invaluable to education practitioners wanting to create inclusive learning environments. This article focuses on the student experience of taking part in a science outreach programme teaching science in prison in England, which was designed to build confidence in students who face challenges in accessing education pathways. Here, former students share their experiences of the programme, as well as other education courses in prison, and offer guidance on best practices for those engaging in outreach or research with the prison population. In particular, their reflections highlight that by creating and maintaining an environment that is accessible, inclusive and relatable, students from all backgrounds are able to engage in course content, and overcome hidden barriers to accessing education. Furthermore, based on their lived experience, the students offer practical advice with regard to improving future access to education in prison. The aim of this article is to give a voice to students in prison about their education experience, highlighting which aspects of this outreach programme (and other education courses) were impactful for them.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".