Harnessing digital workflows for the understanding, promotion and participation in the conservation of heritage sites by meeting both ethical and technical challenges
Bibliographic record
Abstract
Abstract The current application of digital workflows for the understanding, promotion and participation in the conservation of heritage sites involves several technical challenges and should be governed by serious ethical engagement. Recording consists of capturing (or mapping) the physical characteristics of character-defining elements that provide the significance of cultural heritage sites. Usually, the outcome of this work represents the cornerstone information serving for their conservation, whatever it uses actively for maintaining them or for ensuring a posterity record in case of destruction. The records produced could guide the decision-making process at different levels by property owners, site managers, public officials, and conservators around the world, as well as to present historical knowledge and values of these resources. Rigorous documentation may also serve a broader purpose: over time, it becomes the primary means by which scholars and the public apprehends a site that has since changed radically or disappeared. This contribution is aimed at providing an overview of the potential application and threats of technology utilised by a heritage recording professional by addressing the need to develop ethical principles that can improve the heritage recording practice at large.
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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 | 0.069 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".