Evaluating the Cultural Value of Heritage Buildings Based on Analytic Hierarchy Process
Bibliographic record
Abstract
The value of heritage buildings is gradually being emphasized, yet the public's understanding of cultural value remains nebulous.This has resulted in heritage buildings not being adequately protected.In this context, there is an urgent need for the study of evaluating the cultural value of heritage buildings.In this study, the Analytic Hierarchy Process (AHP) combined with a questionnaire survey was used to evaluate the cultural value of heritage buildings, five experts were invited to set the weights of the evaluation indexes of four major categories and 14 subcategories, and 200 valid questionnaires were distributed and successfully collected.It was found that the artistic/aesthetic value of heritage buildings was evaluated the highest, followed by historic value, scientific/technical value the third, and local/place value the lowest.Among the demographic variables, gender, education, and occupation had no significant effect on the evaluation of cultural values, but the age factor showed significant differences.In addition, the public tended to prioritize the artistic/aesthetic value of heritage buildings, while experts gave more importance to their historic value.For these results, the researcher carried out reliability and validity tests, comprehensive analysis, and variance analysis.This study demonstrates that the combination of a questionnaire survey and AHP realizes the quantitative evaluation of the cultural value of immateriality and gets rid of the subjectivity of qualitative evaluation.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".