WOMEN IN BLOCKCHAIN: DISCOURSE & PRACTICE IN THE CO-CONSTRUCTIONOF GENDER AND EMERGING TECHNOLOGIES
Bibliographic record
Abstract
Blockchain is an emerging technology characterized by peer-to-peer value transfer, decentralization, and democratic ideals of consensus. It also has a stark gender problem, with women representing just 14% of those participating in the space. This paper is based on 30 semi-structured interviews with women who work in blockchain, and participant observation at 17 blockchain meetups and conferences. The gendered discourses and practices surrounding blockchain events provide a productive site for examining the social construction of technologies, and more specifically the gendered social shaping of technologies. I use the theoretical lens of technofeminism, which strikes a balance between technophilia and technophobia, to explore the complex ways in which women’s everyday lives and technological change interrelate in the age of digitization. This co-construction approach challenges the prevailing discourses of technologies like blockchain as neutral and value-free. The goal of my study is not to ask or answer questions such as, “why aren’t there more women in blockchain?” or “how can we attract more women into blockchain?” Rather, I examine the gendered sociotechnical relations surrounding blockchain, as exemplified by discourses and practices at meetups and conferences. For instance, ‘by women, for women’ blockchain meetups serve as important spaces of resistance and support, whereas ‘women in blockchain’ panels at blockchain conferences ring hollow as ‘inclusive’ gestures, instead highlighting the exclusive culture at play. My findings explore how women’s identities and experiences are both enabled and constrained, often simultaneously, through participation in the blockchain space.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".