Bibliographic record
Abstract
Second Life, one of the most popular of virtual world was invented by Linden Lab Corporation: its philosophical statement is that Second Life is 'a place where you can turn the pictures in your head into a kind of pixelated reality’ (Rymaszewski, 2007, p. iv). Second Life is, for many reasons to be considered in this essay, a representative example of the (offline/real) world. This paper will argue that virtual worlds such as Second Life are an extension of the real world. Two major questions were posed earlier in this essay: ‘Does the virtual world represent the real world?’ and ‘Is it a refuge for its participants from the real world’. We can answer these questions. First of all, the virtual world is an extension of the real world, because it is built from, and continues to make use of, ideas, meanings, identity categories, performances, narratives and values derived from the real world. Secondly, Second Life is clearly not, in the long term, a refuge for its participants from the real world. Second Life can be understood as a virtual place that allows people to have temporary fun; however, it is only for a moment or for a short period, because the relationship between the virtual and the real world is interpenetrated.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".