VR content and its prosocial impact: predictors, moderators, and mediators of media effects. A systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main purpose of this paper is to explore the prosocial impact of virtual reality (VR) audiovisual content based on a systematic literature review of empirical research on immersive VR media's potential to elicit prosocial behaviors. The illusion of place, verisimilitude, and virtual corporeality are the main elements that underpin the creation of immersive experiences that can turn the user into an active subject of the narrative, engaging with the audiovisual content and feeling the emotions it elicits. A virtual reality system that can offer these three elements provides the means to transform not only the user's sensation of space and reality, but even the users themselves. The question this paper seeks to answer is whether audiovisual VR content can influence an individual's thoughts and feelings about otherness, thereby eliciting prosocial behaviors rooted in a sense of social justice, equality and fairness. To this end, it presents a systematic literature review in accordance with the guidelines of the PRISMA statement, applying a self-deductive coding system based on the Differential Susceptibility to Media Effects Model. The review identifies trends in research on the prosocial potential of VR content, among which perspective taking stands out as one of the most common strategies. In addition, predictors, moderators, mediators, effects, and their correlations are identified in the research reviewed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it