A Micro-Museum Quarter in Sombor, Serbia, as a Sustainable Model for Managing Cultural Heritage in Small Shrinking Cities in Europe
Bibliographic record
Abstract
A museum quarter is praised as a suitable model for the regeneration of global cities. However, it has rarely been implemented in smaller, shrinking cities with a rich heritage, which has become a ‘new normality’ across Europe. These cities usually face institutional, organisational, and economic limitations in their development. Forming a museum quarter at a micro-scale in such city can be a rational model to address these constraints. This paper presents an emerging micro-museum quarter in the historic city of Sombor in Serbia, where the bottom-up level initiated it to deal with the fast shrinkage of the city. /// Le quartier-musée est présenté comme un modèle efficace pour la régénération des grandes métropoles. Néanmoins, il n’a jamais été appliqué aux plus petites villes ou aux villes en décroissance dont les sites patrimoniaux sont pourtant d’une grande richesse. Ces villes deviennent une « nouvelle normalité » dans le paysage européen et font face à des restrictions institutionnelles, organisationnelles et économiques. Construire des quartiers-musées à une micro-échelle dans de telles villes pourrait être une solution pour surmonter ces restrictions. Cet article présente un micro quartier-musée émergeant dans la ville historique de Sombor en Serbie où l’initiative a pris racine au niveau local afin de lutter contre la rapide décroissance de la ville.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".