Saudi Vision 2030: Applying a Sustainable Smart Techno-Cultural Assessment Method to Evaluate Museums’ Performance Post-COVID-19
Bibliographic record
Abstract
UNESCO has defined world cultural heritage as either tangible or intangible cultural heritage. Saudi Vision 2030 strategies is the Culture of Community and Dynamic Supportive Environment, which supports the national identity, maintains the museums, and encourages tourism. This research aimed to assess museums’ performance in four focus areas (sustainability, smart solutions, techno-cultural solutions, and health procedures) during the post-COVID-19 period in Riyadh city. The method consisted of a survey distributed during 04/2021 to stakeholders at the University in two sample case studies: Al Masmak Fort Museum and Riyadh National Museum. The main research aspects of the two samples were compared. The results are average results from survey records and respondents’ responses to survey questions between RNM and MFM to each category: sustainable access (31%) material (31%) water efficiency (29.5%) energy efficiency (32%) smart solutions (31%) and techno-cultural solutions (33%). The health procedures (50.5%) in Table 2. The improvement will be reflected in more advanced and innovative solutions for the museum buildings. could be applied to museum buildings locally and internationally.
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.
How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".