Overview of Social Assessment Methods for the Economic Analysis of Cultural Heritage Investments
Bibliographic record
Abstract
This paper provides an overview of methods for assessing social impacts, their achievements, and possibilities of application in everyday practice for assessing the worth of investments in cultural heritage conservation, as well as its sustainable use. It gives an overview of available methods for social assessment and points to a set of interdisciplinary indicators by which those impacts can be evaluated. Possibilities to use social impact analysis in the assessment of cultural heritage are presented in this paper through two case studies in the Republic of Croatia: the implementation of social evaluation management plan for the old town of Buzet and the evaluation of social effects of investing in the museum Ivana’s House of Fairy Tales. Some qualitative indicators of the collected surveys related to social effects are described, while the analysis of the availability of such indicators and the scientific basis of the collected answers are provided. In conclusion, the contribution of the methodological tools used and social impact assessments in the evaluation of cultural heritage interventions are presented, while suggestions are made for various decision- makers on those broader methods and benefits compared with the use of only financial and economic impact evaluations.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".