Cost–Benefit Analysis in the Evaluation of Cultural Heritage Project Funding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it