Cultural & Knowledge Spaces: the Immersive Museums as a Challenge for KO and the Digital Humanities
Bibliographic record
Abstract
In this paper we discuss the place of museums in Information Science landscape and their historical role in preserving and accessing cultural heritage. Definitions of Culture, cultural heritage and more related concepts are reviewed and examined. The scope of this paper is limited to digital museums and more specifically, immersive museums. The main questions we raise are as follows: How do immersive museums, thanks to digital technologies, redefine the protection and dissemination of knowledge while addressing the socio-cultural needs for inclusion of diverse and neuro-atypical audiences? Do the new immersive mediations risk reducing the transmission of knowledge to simple communication or a spectacle, sometimes far removed from the issues of heritage preservation? Or is it an 'experience' that, while close to reality, diverges from it? The notion of 'reality' in the immersive museum may seem paradoxical: is the technological illusion of reliving the past too far removed from reality, to the point of denying a historical truth, thus inverting the initial objective of preservation and transmission?". How is it possible to assess the preservation and the accessibility of the cultural heritage, namely in museums as a knowledge space? To address these questions, we developed our methodology by analyzing two interviews with representatives from immersive museums that employ diverse technologies and techniques for comparable goals: the creation and establishment of an immersive museum. We conducted an interview with the TNMOC app immersive museum, which resulted from a collaboration between The National Museum of Computing in Milton Keynes (United Kingdom) and the company in charge of creating Version 1 of the immersive tool. The purpose is to show their relevance and evaluate the accessibility to a tangible cultural heritage and its preservation with as special focus on immersive museums. We highlight the assets, the limitations and challenges ahead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".