A Much-Needed Conversation with Women Working in VR
Bibliographic record
Abstract
: This interview explores the underrepresented voices of women in the virtual reality (VR) industry. Through interviews with five women actively working in VR, including artists, community researchers, and industry professionals, the discussion sheds light on their experiences, creative processes, recurring themes, and ongoing challenges. From Linda Zhang's exploration of social dreaming and community co-creation to Michelle Cortese's efforts in making virtual spaces safer for femaleidentifying individuals, each woman brings a unique perspective to the conversation. The chapter delves into their candid discussions about the trials and tribulations they face in the VR industry, aiming to inspire and empower other women considering entering this evolving field. Keywords: virtual reality, women in technology, diversity in VR, gender disparity, industry studies I have been studying virtual reality (VR) for seven years and have noted, time and again, that the voices of women—and particularly racialized women—are missing from conversations about the VR industry and its evolution. When working in virtual reality, women and female-identifying artists and professionals face many barriers, such as income equality, discrimination, and accessibility issues. This chapter started as an honest and open conversation about how women were making VR work. I interviewed five women who are currently working in VR about how they are creating work, the themes they return to, and the challenges they continually face. These women came to this conversation with different perspectives and agendas. Some are artists (such as Nadine Valcin and Cat Bluemke), others are gamers (Paloma Dawkins), community researchers (Linda Zhang), and individuals who work in the industry, at Meta (Michelle Cortese). Linda Zhang is an assistant professor in the School of Interior Design at Toronto Metropolitan University (TMU). She creates VR with her community in Toronto's Chinatown and is interested in social dreaming and community co-creation, and how they allow her to think critically about culture, meaning, identity, and memory. I was following her ChinaTOwn Project and its many iterations over the last three years. Michelle Cortese is currently the Design Operations Lead at Meta and teaches at New York University (NYU). I came across her book chapter “Designing Safe Spaces for Virtual Reality” and needed to hear more about her work on making virtual spaces safer for female-identifying people.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".