Space Echo 2.0: Exploring Social Interaction Through Communication Barriers in VR
Bibliographic record
Abstract
Space Echo 2.0 is an experimental media art project that introduces deliberate disruptions into avatar interactions within a multiplayer virtual reality (VR) environment (see Figure 1).These interruptions are designed to prompt reflection on the nature of genuine communication while offering novel, paradoxical conversational experiences.Drawing on a range of influences-from social VR platforms [Maloney et al. 2020] and experimental game design [Soderman 2021] to psychological research [Eisenberger et al. 2003], mythological narratives, and Bertolt Brecht's theatrical theory [Brecht 1960]-the project uses intentional communicative disruption as a lens through which to reconsider connection.Set within a dreamlike, symbolic virtual stage shared by two participants, the experience centers on the Reverse Jetpack, a core mechanic that moves an avatar in the opposite direction of their gaze each time they speak.The more participants attempt to communicate verbally, the more physical distance is created between them.This enforced separation paradoxically fosters emotional intimacy, highlighting the tension between the desire for connection and its inevitable distortion.Scattered throughout the environment are AI-generated images and audio fragments.As users approach these elements, whispered, looped narratives are triggered, offering a sensory encounter with miscommunication, repetition, and distortion themes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".