The Museum as a Tool of Connection. The Case of the Diffused Museum of the University of Milan-Bicocca
Bibliographic record
Abstract
The University of Milan-Bicocca is a recent institution, created in the last quarter of the twentieth century, placed in a new open campus in a completely renewed area in the Northern part of Milan, formerly occupied by huge industrial facilities. From its creation, the University has paid great attention to activate connections in this transformed territory with its old and new residents. Meanwhile, the University has started a process of connection between the different departments to improve the knowledge about tangible and intangible cultural heritage, and to share experiences, practices and research on it. In 2019, the University identified, through a diffused museum, an adequate tool to activate these connections among and outside the departments. The museum aims to improve collaborative practices among the different parts of the University (the departments but also the library and the archives) and to involve the community of ‘users’ of the territory such as; residents, students, personnel of the university, occasional visitors, in participative museum processes. The museum is currently in the primary stages of activation (digitalization of the collections, designing of the ‘diffused’ displays etc.) and it is hosting some temporary activities to experiment with different ways to connect University/collections/territory. In this presentation we will introduce some relevant actions in this direction and analyze the results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".