“Words to receive. Words to be received”: reflections on the Intercultural City museum work
Bibliographic record
Abstract
Although diversity has always been a fundamental characteristic of human societies, now more than ever it has become central to the political and research agenda. The question of how we can live together while enjoying our differences is a fundamental issue of our time, and the city is viewed as the most promising site to negotiate identities. That being so, what is the role of museums? How can local museums develop interventions that address local cultural diversity issues? In the first part of the article, I introduce the idea of “Intercultural City museum work.” I present a metadesign framework that aims to help museums emphasize the impact of diversity work on their local contexts, proposing the Intercultural City approach as a reference point. In the second part of the article, I describe the “Intercultural City museum work” and on using the metadesign framework with reference to MUST-Museo del Territorio Vimercatese, a civic museum on local history and identity in Vimercate, a town in the metropolitan area of Milan. Immigration to the geographical area over the past few decades and the resulting cultural diversity are neither reflected in the museum collections nor the permanent exhibitions. As a result, the museum decided to address these topics through services, events and special projects. In particular, I describe the exhibitionWords to Receive. Words to be Received, designed and created by COI-Centro Orientamento Immigrati—a local immigrants’ resource centre—with the museum.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.042 | 0.070 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".