ETHICAL FRAMEWORK FOR HERITAGE RECORDING SPECIALISTS APPLYINGDIGITAL WORKFLOWS FOR CONSERVATION
Bibliographic record
Abstract
Abstract. Recording the physical characteristics of historic structures and landscapes is a cornerstone of preventive maintenance, monitoring and conservation. The information produced by such workflows guides decision-making by property owners, site managers, public officials, and conservators. Rigorous documentation may also serve a broader purpose: over time, it becomes the primary means by which scholars and the public apprehend a site that has since changed radically or disappeared. The development of ethics principles (or a code of ethics) applicable to the heritage recording specialist in their conduct, responsibilities, professional practice and for the benefit of the public and communities is of paramount importance. As indicated by Smith (2019), “the values and principles inherent in the technology itself are more sharply diverging for a reckoning: we must now address not just the practical considerations of the technology we use, but also its moral and ethical implications. If we don't, we risk compromising the values of the heritage we serve.” This means that it is important that the practice allow for better planning, recording, processing and dissemination of digital workflows for the conservation of historic places. Also, digital products should improve the practice, including sharing and preserving records among heritage organizations around the world. This contribution seeks to establish a framework to review and apply ethical concepts to improve the field of digital heritage recording.
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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 | 0.129 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.062 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".