Hybrid Space: An Emerging Opportunity That Alternative Reality Technologies Offer to the Museums
Bibliographic record
Abstract
In addition to the actual space and virtual space, there seems to be a third type that can be called hybrid space. Hybrid space borrows the power of information to empower the physical space around us using technologies such as augmented reality, virtual reality, and augmented virtuality. Hybrid space has been explored and conceptualized in the literature, but it has yet to reach its potential as an effective medium in museums. However, it seems to have quite a few advantages to be employed in the museums to attract more people, motivate a higher participation, and change the existing paradigms by reinventing museums. This article applies a qualitative content analysis to a sample of publications to conceptualize hybrid space and position it in a suggested continuum of space. Moreover, the role of technology and considerations about the museum content in a hybrid space are explored. The aim of this theoretically and technologically oriented article is to promote the professional use of the hybrid space in museums.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".