Thinking through the tool: collaborative archaeological bodywork in immersive virtual reality
Bibliographic record
Abstract
Thanks to currently available very high-resolution three-dimensional (3D) models via photogrammetric techniques as a primary method of archaeological documentation, constructing immersive, high-fidelity simulacra is imminently possible. This paper considers how the scale at which the human body interacts with immersive digital models is especially important for understanding the affordances and ergonomics of past things and places. The implications of this isometry between archaeological objects of analysis and emerging capabilities to interact with them through digital surrogates in the present are manifold. By enabling interaction with objects and contexts in immersive virtual space, such observational experiences create in silico engagements that are repeatable, distributable, and collaborative. In particular, it is the collaborative capacity of this technology that this paper explores using online immersive virtual reality (iVR). Collaborative online iVR is used in this research as a key instrument for enhancing understanding and reinterpreting the digital records of two archaeological sites under excavation in Peru. The case studies analyzed show a variety of cultural, geographic, and temporal contexts in the Andean region, which illustrates the broad potential of iVR for archaeological hermeneutics. Through iVR frameworks, the authors engage with embodied reconsiderations of Catholic ritual spaces within a planned colonial town in the southern Peruvian highlands and the pre-Columbian site of Huaca Colorada on the north coast. Synchronous scalar experiences that privilege the affordances of architectural space within digital models create opportunities for embodied experience and collaborative dialogue. A fundamental argument is the capacity to digitally inhabit these places and manipulate materials holds subtle as well as profound epistemological and hermeneutic implications for archaeological knowledge construction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".