“Alexa, are you a feminist?”: Virtual Assistants Doing Gender and What That Means for the World
Bibliographic record
Abstract
Alexa is an Artificial Intelligence Virtual Assistant whom we freely accept into our homes, where she listens to our needs and obliges our every command. Through these interactions, Alexa performs a gender and does digital domesticity, acting as a host into a new digital era. By anthropomorphizing the robot as female, Alexa’s creators imposed womanhood on her, which is neither a natural nor inevitable political act. Using theories of gender performance, this paper explores the ways in which Alexa obeys commands, working to hard-code a connection between women and subservience. In an attempt to appease male fantasies of heterosexuality, she serves up gentle feminism and contributes to histories of erasure, further removing women’s bodies from the circumstances of production. Technology and media representations have power over users, perpetuating idealistic and unrealistic forms of femininity, simply because we have come to expect the same from the women in our lives.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".