Understanding and Mitigating New Harms in Immersive and Embodied Virtual Spaces: A Speculative Dystopian Design Fiction Approach
Bibliographic record
Abstract
Seeking novel approaches to understand and mitigate emerging and understudied new harms in immersive and embodied social spaces is a critically needed HCI and CSCW research agenda for achieving safer online environments in the future. In this work, we present and discuss the results from a CHI 2024 workshop in which 26 experts engaged in a speculative dystopian design fiction activity. Through the structured design fiction exercise, our participants collectively created six design fictions along four main themes highlighting our shared concerns in this problem space. We contribute to CSCW and HCI research by demonstrating the novelty and value of using speculation and design fiction as a methodological tool to engage with research in this emerging problem space. The identified themes and created design fictions also help us speculate plausible and desirable futures regarding new harms in embodied and immersive virtual spaces in the first place, which will inform our future research on envisioning and identifying potential solutions to prevent these harms from becoming reality.
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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 | 0.051 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".