DIVE INTO HERITAGE: A DIGITAL DOCUMENTATION PLATFORM OF WORLD HERITAGE PROPERTIES IN THE ARAB STATES REGION
Bibliographic record
Abstract
Abstract. The world celebrated the 50 years of the World Heritage Convention. With more than 1000 cultural and natural sites, the 1972 World Heritage Convention is the most widely recognized. It has provided a framework for identifying, documenting, protecting, and managing the world's cultural and natural heritage with Outstanding Universal Value (OUV). The theme to mark this anniversary is: “The Next 50: World Heritage as a Source of Resilience, Humanity and Innovation”. These are the topics that have inspired the World Heritage Centre to develop, together with the Member States in the Arab Region, an online platform that leverages digital technologies to safeguard and promote the UNESCO World Heritage sites and its related intangible heritage, and transmit them to future generations. This paper discusses the current state of digital documentation of cultural heritage and the related projects/initiatives in the Arab States region. It presents the UNESCO Dive into Heritage initiative and its first outcomes. It concludes with lessons learned and future steps for the next phases of the project. First outcomes have revealed the big challenge of 3D data integration and the need to accompany the implementation stages of the project with capacity building.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".