Unestablished Boundaries: The Capabilities of Immersive Technologies to Induce Empathy, Tell Stories, and Immerse
Bibliographic record
Abstract
This article presents a critical viewpoint on the existing research to establish the boundaries of immersive technologies, such as virtual reality, exploring distinctions between sensorial and mental experiences and highlighting the influence of technological determinism in this scholarly domain. The analysis reveals a lack of established conceptual structures for categorizing distinct types of immersion, emphasizing that immersion is not universal and is not inherently technological. In particular, it highlights that, fundamentally, immersive technologies are not designed to immerse into narratives. As a result, this article suggests a dual cognitive framework of immersion to explain the nature of different immersive experiences. The article also critically addresses ethical concerns related to identity tourism and argues against the oversimplification of complex psychological processes, emphasizing the overreliance of the existing studies on visual or technological stimuli. To avoid this, the article suggests a way to avoid technological determinism in relevant conceptualizations. Overall, the article scrutinizes the assumptions associated with immersive technologies, offering insights into their capabilities to stimulate senses and vividly inform, contributing to a nuanced understanding of their effects and ethical implications.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".