How Collaboration Context and Personality Traits Shape the Social Norms of Human-to-Avatar Identity Representation
Bibliographic record
Abstract
As avatars have evolved from simple digital representations into extensions of our identities, they offer unprecedented opportunities for self-expression and customization beyond the physical world limitations. While virtual platforms foster new forms of identity exploration, social norms still play a crucial role in defining what is considered appropriate in these environments. In this study, we surveyed 150 participants to investigate social norms surrounding avatar modifications, examining how perspectives, contexts, and personality traits influence attitudes toward appropriateness. Our findings reveal that avatar modifications are generally viewed as more appropriate when considered from a partner's perspective, especially for changeable attributes. However, these modifications are perceived as less acceptable in professional settings such as workplaces. Additionally, individuals with high self-monitoring tendencies tend to be more resistant to changes, while those scoring higher on Machiavellianism are more accepting of changes, particularly regarding unchangeable attributes and emotional expressions. These findings provide valuable insights for platform developers and designers, highlighting the importance of implementing context-aware customization options that balance core identity elements with personality-driven preferences, thereby enhancing user experiences while respecting social norms.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".