Knee-High Boots and Six-Pack Abs: Autoethnographic Reflections on Gender and Technology in Second Life
Bibliographic record
Abstract
In this paper, we explore the experience and performance of gender online in Second Life, currently one of the most popular virtual world platforms. Based on two collaborative autoethnographic projects, we propose that gender has to be explored at the intersection between our own situated perspective and the vision embedded in the social and technical infrastructure of the virtual world. For us, the visual element of a 3D world further frames the representation and performance of gender, while technical skill becomes a crucial factor in constructing our ability to play with this performance. As we recollect and interrogate our own experiences in SL, we argue that the relation between gender and virtual worlds is a complex and multifaceted one, proposing our positioned account of experiencing this relation. It is critical, we suggest, that studies of mediated experience in virtual worlds take into account the position of the researcher in ‘real’ life (IRL) as well as the dominant discourses of the environment they are immersed in. In this we must also be critical, of ourselves, our assumptions, as well as the environment itself.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".