The Stigma Machine: A Study of the Prosocial Impact of Immersive VR Narratives on Youth in Spain and Canada
Bibliographic record
Abstract
Virtual reality (VR) is increasingly employed to create immersive, interactive audiovisual narratives that accentuate emotion, storytelling, and user engagement. By harnessing the potential of VR, these avant-garde narratives aim to instill values of equity, justice, and fairness. This article critically examines the largely unsubstantiated assertion that VR is the ultimate tool for fostering empathy by means of a qualitative evaluation of the influence of prosocial VR audiovisual narratives. The study involved the production of the first episode of The Stigma Machine, a VR short film series in both traditional 2D and immersive VR formats, in a two-pronged production approach designed to examine the effects of the film on a sample of 44 university students from Spain (n = 22) and Canada (n = 22). The participants were segregated into two groups: Group 1 (1st VR Condition) viewed the VR experience first, followed by the traditional version, while Group 2 (1st Video Condition) viewed the two formats in reverse order. Data was collected before, during, and after viewing, using standardized questionnaires (interpersonal reactivity index, basic empathy scale, and Igroup presence questionnaire) and electroencephalogram devices to monitor brain activity. The dependent variables included: empathy, assessed using the interpersonal reactivity index and basic empathy scale surveys; electroencephalogram brain activity measures, indicating engagement, excitement, focus, interest, relaxation, and stress; presence, evaluated using the Igroup presence questionnaire; and various outcome variables. The results reveal no significant differences in presence and no significant changes to the empathy scores. The findings point to a need to focus more on narrative design and audiovisual content creation strategies than on VR technology itself.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".