Bibliographic record
Abstract
This chapter explores the history, state-of-the art, and interactive aesthetic potential of “SL-Bots”. SL-Bots are avatars (i.e. “agents”) that are designed and controlled using Artificial Intelligence (AI) in Second Life. Many of these SL-Bots were originally created in Second Life for purposes such as: rudimentary chatinventory management and copying, asset curation, embodied customer service, generic responsive environments, scripted objects, or as proxy-audience members (aka “campers”). However, virtual performance and installation artists – including two of the chapter's authors [ca. 2011-present] - have created their own SL-Bots for aesthetic purposes. This chapter suggests ways in which SL-Bots are gradually being extended beyond their conventional applications as avatar-placeholders. This book chapter concludes with the speculation that future virtual agents (including next generation SL-Bots) might one day transcend their teleological aesthetic purpose as mere automated-objects by evolving into more complex autonomous aesthetic personas.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".