Theorising 3D Visualisation Systems in Archaeology: Towards more effective design, evaluations and life cycles
Bibliographic record
Abstract
3D visualization in archaeology has become a suitable solution and effective instrument for the analysis, interpretation and communication of archaeological information. However, only few attempts have been made so far for understanding and evaluating the real impact that 3D imaging has on the discipline under its different forms (offline immersive and not immersive, and online platform). There is a need in archaeology and cultural heritage for a detailed analysis of the different infrastructural options that are available and a precise evaluation of the different impact that they can have in reshaping the discipline. To achieve this, it is important to develop new methodologies that consider the evaluation process as a fundamental and central part for assessing digital infrastructures. This new methods should include flexible evaluation approaches that can be adapted to the infrastructure that need to be assessed. This paper aims at providing some examples of 3D applications in archaeology and cultural heritage and describing how the selection of the infrastructure is related to specific needs of the project. This work will describe the different applications and propose guidelines and protocols for evaluating their impact within academia and the general public.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".