MAKING VIRTUAL ARCHEOLOGY GREAT AGAIN(WITHOUT SCIENTIFIC COMPROMISE)
Bibliographic record
Abstract
Abstract. In the past two decades or so, digital tools have been slowly integrated as part of the archaeological process of information acquisition, analysis, and dissemination. We are now entering a new era, adding the missing piece to the puzzle in order to complete this digital revolution and take archaeology one step further into virtual reality (VR). The main focus of this article is the methodology of digital archaeology that fully integrates virtual reality, from beta testing to interdisciplinary teamwork. We briefly discuss data acquisition and processing necessary to construct the 3D model, the analysis that can be conducted during and after the making or creation of the 3D environment and the dissemination of knowledge. We explain the relevance of this methodology through the case study on the intendant’s palace, an 18th century archaeological site in Quebec City, Canada. With this experience, we believe that VR can prompt new questions that would never have occurred otherwise and can provide technical advantages in terms of gathering data in the same virtual space. We conclude that multidisciplinary input in archaeological research is once again proven essential in this new, inclusive and vast digital structure of possibilities.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".