Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
Bibliographic record
Abstract
Abstract This research is dedicated to a comprehensive exploration of the strengths and weaknesses inherent in the legal and institutional measures established to safeguard cultural heritage in the Republic of Serbia against the adverse effects of disasters, including earthquakes, landslides, rockfalls, floods, torrents, storms, hail, and forest fires. The study seeks to identify key challenges and shortcomings within the existing legal and institutional framework while also highlighting and analyzing best practices and potential avenues for improvement in the protection system. The research posits a preliminary hypothesis suggesting that significant challenges exist within the current framework, potentially hindering effective response and recovery efforts following natural hazards. Data collection involved semi-structured interviews with field experts and an in-depth analysis of existing documentation. These methods were aimed at gathering critical data and insights to enhance the understanding of systemic issues and contribute to developing practical, viable solutions. The analysis and processing of the collected data were conducted using ATLAS. ti software, enabling a detailed and systematic examination of qualitative information. Moreover, assessing the current capacity of institutions to respond swiftly and effectively to natural hazards that threaten cultural heritage formed a central aspect of this study. The findings reveal notable deficiencies in the legal framework, inadequate institutional capacities, limited resources, and insufficient training for disaster response. The results underscore the pressing need for improved inter-institutional cooperation and the development of technical and logistical capabilities. To address these issues, the study recommends aligning legal frameworks with international standards, securing increased funding for technical resources, and implementing specialized training programs for institutional staff. This article makes a significant contribution to advancing the understanding and enhancement of the cultural heritage protection system in Serbia, offering actionable insights and a robust foundation for further research and strategic development in this critical area.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".