Virtual Reality (VR) and the Role of Technology in Fashion Tourism
Bibliographic record
Abstract
This book chapter aims to comprehensively explore and analyse the intersection of virtual reality (VR) and technology in fashion tourism. By researching the immersive experiences offered by VR, this chapter aims to elucidate how technology transforms the landscape of fashion tourism, providing readers with insights into virtual fashion shows, virtual boutique tours, and the global accessibility facilitated by these advancements. Additionally, this chapter will investigate the role of augmented reality (AR) in reshaping the interactive aspects of fashion tourism, such as virtual try-ons (VTO) and personalised shopping experiences. Secondary data and an extensive literature review will be explored to achieve the purpose of the study. This research’s evident findings will also further understand the role of VR and technology in fashion tourism. The objective is to offer a nuanced understanding of the symbiotic relationship between VR, technology, and fashion tourism, shedding light on its impact on consumer engagement, industry dynamics, and the future trajectory of fashion experiences. This study will contribute to the body of knowledge regarding VR and the role of technology in fashion tourism by providing in-depth information that will benefit scholars, tourism planners, and policymakers. The limitation would focus on the current evolution, which involves VR and the role of technology in fashion tourism. This is where scholars, organisations, and policymakers must take the proper action and responsibility to continuously ensure integration between VR, technology, and fashion tourism as technology today is rushing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".