Second Life Surveillance: Power to the People or Virtual Surveillance Society?
Bibliographic record
Abstract
In the virtual world of Second Life, almost all content is user-generated and users retain a significant amount of autonomy. Given this freedom, participants are able to create, acquire, and use their own forms of surveillance. With accessible, affordable, and easy to use options, ordinary participants regularly deploy surveillance to protect themselves and their interests, businesses, and property. This paper examines the applications, awareness, and climate of surveillance within the virtual world. It argues that the socio-technical environment of Second Life facilitates problematic covert surveillance, but also makes possible forms of resistance. Examples of surveillance technologies and applications reveal the ways in which the virtual world supports and obscures surveillance, making these practices challenging for participants to detect, analyze, and respond to. However, the recent controversial case of RedZone not only highlights some of the most pressing issues with Second Life surveillance, but also the ways in which participants are able to respond to perceived threats. Although ostensibly intended to track Internet protocol (IP) addresses in order to prevent in-world harassment and theft, participants expressed widespread concern at the program’s potential for associating online and offline information and for linking together different avatars. In doing so, awareness was raised, protests were made, and counter-technologies were developed. These responses point to the ways in which the virtual world makes possible resistance to surveillance practices, especially when they are seen as a threat or intrusion.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".