Introduction to the Minitrack on Immersive Technologies in Business
Bibliographic record
Abstract
Immersive technologies integrate virtual content with the physical environment and create an immersive experience for users.Typical examples of immersive technologies include augmented reality (AR), virtual reality (VR), and mixed reality (MR).Other immersive technologies with developing applications and potentials include extended reality (XR), digital twin technology, holography, and the metaverse.These technologies have long captivated public attention and imagination, and are now coming into our daily life.They have been adopted in application areas such as entertainment, retailing, ecommerce, education, gaming, tourism, military, and medicine (Javaid & Haleem, 2020;Radianti et al., 2020;Tom Dieck & Han, 2022).Immersive technologies are increasingly transforming our experience in various aspects of life and business.With further maturity of the technologies and reduction in cost, they are on the verge of a more pervasive entrance into our life and are expected to revolutionize how we interact with the world and digital content.Research on the adoption, usage, and impact of immersive technologies in business has drawn growing interest in recent years.This minitrack aims to provide a discussion forum for involved and interested researchers to share their developing work and foster collaborative efforts in this field.This minitrack attracted two research paper submissions and one is accepted for publication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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".