Bibliographic record
Abstract
This paper addresses the problem of how prisons, as hyper-visible yet socially opaque institutions, are represented in the public sphere, and how design education can intervene in transforming collective perceptions through participatory and communicative practices. It analyzes the work of Imagis Lab (Department of Design, Politecnico di Milano), which since 2019 has been conducting action-research in Milanese prisons—namely the Seconda Casa di Reclusione di Bollate and the Casa Circondariale 'Francesco di Cataldo' (San Vittore)—exploring the role of communication design as a relational process, and storytelling as both a tool to activate listening processes and a collaborative practice to investigate and build new imaginaries. Central to this investigation is Off Campus San Vittore, a university-led space within the San Vittore prison developed through Polisocial, the university’s social responsibility program, which aims to build reciprocal knowledge between the prison and the city through interdisciplinary, site-specific research. Focusing on a participatory design education project conducted during the winter semester of 2023, the paper examines how 40 master's students in Communication Design engaged with the prison context to co-create transmedia narratives intended to shift public consciousness about incarceration. The first section of this paper introduces the conceptual lens of entanglement that emerged through this educational experience, followed by a discussion of the prison as a paradoxically transparent institution. The second section presents the pedagogical process and two student-produced transmedia documentaries, analyzing 1) how the design teams have activated new societal connections and heightened students' awareness of values, conflicts, and agency within these systems, and 2) sparking transformative discourses and engagement projects aimed at cultural and narrative change.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".