Pains of imprisonment in a “lock em’ up” video game: implications for a peacemaking discourse through new media experiences
Bibliographic record
Abstract
A limited body of literature has explored popular media portrayals of the prison experience. Much of this literature has focussed on film and television. Scant literature has considered new forms of media such as video games’ portrayals of the prison experience. In the current inquiry we examine the computer simulation game, Prison Architect, with respect to how its interactive experience has the potential simultaneously portray and problematize pains of imprisonment, and how these portrayals and problematizations may prompt a public discourse surrounding prison, particularly from a peacemaking perspective, even if the game itself does not incorporate concepts such as restorative justice. To conduct this analysis, we examine game-developer video blogs that relayed information about the game as it was developed (e.g., game content, rationale for creation, and embedded political, social and philosophical orientations toward prisons, prisoners, and the prison-industrial complex). Ultimately we link pains of imprisonment in Prison Architect to the broader societal discourse surrounding rationales for incarceration (i.e., retribution, incapacitation, and rehabilitation) and consider implications for prison themed games, particularly those such as simulation games that afford players a broad degree of freedom, as vehicles through which to engage the public in discourse about prison that can adopt a more human-centered, peace-oriented approach.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".