Cost–Benefit Analysis in the Evaluation of Cultural Heritage Project Funding
Bibliographic record
Abstract
Cultural heritage has, for a long time, been considered a source of wealth and well-being for economies. Currently, considerable investments have been allocated for its renewal and maintenance that often surpass the budgets of owners, local communities, and other interested users. Cultural heritage valorisation is expensive and is a great economic challenge. Infrastructural investment, i.e., conservation and restoration, are just one part of the total costs of cultural heritage preservation, while other investments relate to regular operation and maintenance. One of the most difficult decisions for those who design the cultural heritage restoration projects is how to finance them, i.e., what the most efficient financial instruments are for renewal of cultural heritage. These assumptions have instigated interest in the evaluation of services resulting from common good functions of cultural heritage, such as economic, educational, historical, technological, ecological, and climate, as well as tourism and recreational. Therefore, this article starts from the analysis of potential funding sources for cultural heritage through the European Union (EU) funds; a method of economic evaluation of the return on investments and cost–benefit analysis is suggested as a method that should be used in decision making on these interventions.
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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 | 0.069 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".