Virtual worlds: Relationship between real life and experience in Second Life
Bibliographic record
Abstract
Due to the unique applications of virtual reality in many modern contexts, Second Life (SL) offers inimitable opportunities for research and exploration and experiential learning as part of a distance learning curriculum assignment. A review of current research regarding SL examined real world social influences in online interactions and what the effects on users may be. This aids students in understanding the social constructionist perceptions and worldview of those persons they may serve in social services. This suggests the importance of developing an understanding of the relationship between users’ real life (RL) and their SL. Some research has begun to reveal the effectiveness of telecommunication and computer simulation with certain clients in the fields of mental health and social work, yet there is a lack of sufficient research done within the context of virtual worlds. The current study surveyed users of several educationally and health focused SIMS (simulations) as to what motivates their SL and RL interactions. The data explores associations between users’ RL and their SL in several areas,potentially addressing the future role of educating social work students regarding research methodology in online virtual reality interactions. Implications for social work are discussed including engaging clients using incentives for social participation built into the SL milieu.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".