Unmediated cultural heritage via Hyve-3D: Collecting individual and collective narratives with 3D sketching
Bibliographic record
Abstract
Cultural heritage is traditionally mediated through institu- tional bodies that are authorised to broadcast heritage information, whereas new media technologies such as social media platforms con- tinue to enforce individual storytelling and information sharing. Therefore GLAMs (Galleries, Libraries, Archives and Museums) have to cope with a shift of public interest from their services to more ac- cessible, entertaining and democratic engagements available as ‘liv- ing’ media. Unmediated cultural heritage is the paramount aim of this work and, in a theoretical sense, a utopia for generation of authenticity or meaning-making. Within the realm of digital heritage, this study explores the nature of engagement with cultural heritage using an in- novative means. In this phase of the research, a photogrammetric model of Kashgar’s narrow alleys is deployed in a system, called Hy- brid Virtual Environment 3D (Hyve-3D). Via its 3D cursor technolo- gy, the concept of unmediated cultural heritage is unfolded through active participation, collaboration and interaction. Thus, in the context of heritage, this research explores a hitherto undocumented frontier of Hyve-3D designated to immersive collaborative 3D sketching.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".