PROTECTING UNESCO WORLD HERITAGE PROPERTIES'S INTEGRITY: THE ROLE OF RECORDING AND DOCUMENTATION IN RISK MANAGEMENT FOR PETRA
Bibliographic record
Abstract
Abstract. Risk management – as it has been defined – involves the decision-making process following a risk assessment (Ball, Watt, 2003). It is the process that involves managing to minimize losses and impacts on the significant of historic structures and to reach the balance between gaining and losing opportunities. This contribution explains the "heritage information" platform developed using low-cost recording, documentation and information management tools to serve as container for assessments resulting from the application of a risk methodology at a pilot area of the Petra Archaeological Park, in particular those that permit digitally and cost effective to prepare an adequate baseline record to identify disturbances and threats. Furthermore, this paper will reflect on the issue of mapping the World Heritage property's boundaries by illustrating a methodology developed during the project and further research to overcome the lack of boundaries and buffer zone for the protection of the Petra World Heritage site, as identified in this project. This paper is based on on-going field project from a multidisciplinary team of experts from the Raymond Lemaire International Centre for Conservation (University of Leuven), UNESCO Amman, Petra Development Tourism and Region Authority (PDTRA), and Jordan's Department of Antiquities (DoA), as well as, experts from Jordan. The recording and documentation approach included in this contribution is part of an on-going effort to develop a methodology for mitigating (active and preventive) risks on the Petra Archaeological Park (Jordan). The risk assessment has been performed using non-intrusive techniques, which involve simple global navigation satellite system (GNSS), photography, and structured visual inspection, as well as, a heritage information framework based on Geographic Information Systems. The approach takes into consideration the comparison of vulnerability to sites with the value assessment to prioritize monuments at risk based on their importance of significance and magnitude of risk, in order for the authorities to plan more in-depth assessment for those highly significant monuments or areas at risk. A decision tool is envisaged as outcome of this project.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".