Virtual Archaeology, Virtual Longhouses and "Envisioning the Unseen" Within the Archaeological Record
Bibliographic record
Abstract
We are of an era in which digital technology now enhances the method and practice of archaeology. In our rush to embrace these technological advances however, Virtual Archaeology has become a practice to visualize the archaeological record, yet it is still searching for its methodological and theoretical base. I submit that Virtual Archaeology is the digital making and interrogating of the archaeological unknown. By wayfaring means, through the synergy of the maker, digital tools and material, archaeologists make meaning of the archaeological record by engaging the known archaeological data with the crafting of new knowledge by multimodal reflection and the tacking and cabling of archaeological knowledge within the virtual space. This paper addresses through the 3D (re)imagination of a 16th century pre-contact Iroquoian longhouse, by community paradata blogging and participatory research, how archaeologists negotiate meaningmaking through the use of presence and phenomenology while also addressing the foundations of the London Charter: namely agency, authority, authenticity and transparency when virtually representing constructed archaeological knowledge. Through the use of Ontario Late Woodland longhouse excavation archaeological data, archaeological literature, historical accounts and linguistic research in combination with 3D animation and visual effects production methodologies, and engaging this mental construction made real in virtual reality by deploying these assets in a real-time gaming and head mounted immersive digital platform, archaeologists can interact, visualize and interrogate archaeological norms, constructs and notions. I advocate that by using Virtual Archaeology, archaeologists build meaning by making within 3D space, and by deploying these 3D assets within a real-time, immersive platform they are able to readily negotiate the past in the present.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".