The Museification of the Former Prisons: International Experience and Russian Reality
Bibliographic record
Abstract
This article is dedicated to several modern approaches to the cultural museification process of former prisons. “The fate” of closed and no longer working prisons is the subject of discussion between state authorities, business and civilian population. The result of the prison transformation directly depends on the funding sources. Currently, there are multiple examples of prison buildings being used as cinema settings, social housing, hotels and hostels, and shopping centers. Recently, visiting old prisons has become a popular destination for cultural and educational tourism, so the problem of creating museums on their territory has attracted fixed scientific attention. The research on the museification process of prison facilities involves the study of the processes of repurposing former places of detention; studying the features of excursion organizing and analyzing the impressions of prison museum visitors. This article analyzes and emphasizes Canadian and French cultural experiences of prison museification, including their strategies of museification and tour guides classification. As an example of the implementation of a comprehensive approach to the preservation of liberty deprivation places as the objects of cultural heritage, the French Virtual Museum of Justice, whose exposition is constantly being updated, is considered. Prisons that have been turned into Museums are multifaceted objects, and the goals of preserving and exploring these heritage sites also vary: from studying architectural specific of the building to perpetuating the memory of the innocent.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".