Special Issue on Exploring Identity, Emotions, and Learning in Virtual Environments: An Introduction
Bibliographic record
Abstract
"This special issue explores how virtual environments (VEs) can be used to elicit emotions and perspective taking as well as help users to learn and explore their virtual avatar and physical world identities in interesting ways. VEs are computer-generated environments of real or imaginary content and include games and simulations. The articles selected for this special issue provide novel and valuable insights into a wide-range of VEs, including immersive computer-based (e.g., Second Life), as well as commercial games (World of WarCraft). The authors used a variety of psychological theories and constructs (e.g., five factor model of personality, self-determination theory, self efficacy, empathy, and presence) to generate an interesting set of research questions to frame the use and impact of the digital environments presented in each article. The result is a special issue that explores the effect of everything from environment-external tools to embedded and customizable features of these environments on users interactions with them and the resulting psychological (e.g., affective) and learning outcomes [...]"@eng
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.022 |
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".