Concurrent and sustainable development of a local-scale digital heritage inventory through action research at Bat, Oman
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present a concurrent implementation of sustainable inventory for the UNESCO World Heritage sites of Bat, Al-Khutm and Al-Ayn in the interior of Oman. Design/methodology/approach – A digital heritage inventory (DHI) was developed through an action research to realize demands of the local agent and to co-design the solution. The Ministry of Heritage and Culture of Oman, the local agent, demanded to have archaeological information of the sites shared with foreign expeditions, which had worked at the sites for decades, for efficient heritage management, scientific research, outreach, and education. To this end, the Bat Digital Heritage Inventory (BatDHI) was implemented by a combination of network-access-ready database application, open source geographical information systems, and a web-based map service to incorporate and visualize previous works, which were concurrently cross-checked and updated by ground-truth surveys. Findings – The online inventory made it possible to update information during archaeological fieldwork in real time and accelerated the decision-making process in heritage management by prompt data updates and visualization. Research limitations/implications – The DHI is extendable for other sites or regions. It should also be considered to install Arches, an open-source suite of digital heritage inventories. Practical implications – The BatDHI was implemented through the action research mentioned in the design/methodology/approach section and yielded the implications mentioned in the findings section. Originality/value – This paper is a challenging application of transdisciplinary approach to the sustainable heritage management, in which researchers and societal stakeholders collaborate for co-design of research agendas, co-production of knowledge, and co-dissemination of outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".