Investigating the Impact of Gender Stereotypes in Authority on Avatar Robots
Bibliographic record
Abstract
We investigate how gender stereotypes in authority influence the perceptions and behavior of avatar robots operators and their interlocutors. Gender stereotypes, which typically place men in more authoritative positions than women, are present in not only inter-human but also human-robot interaction. As avatar robots become more integrated into our lives and serve for diverse usages, they may be utilized in positions where they require authority. We study how avatar robot gender and operator gender affect expressions and perception of gender stereotypes in a customer service scenario with 41 pairs of participants. Operators controlled binary gendered avatar robots one at a time, acting as shopkeepers that had to assert authority over customers behaving improperly. The operators perceived their authority to be higher with male avatar robots compared to female ones, regardless of operator gender. We did not detect an effect on customer's perception of the shopkeeper's authority. While less than half of operators and customers perceived authority for reasons related to traditional gender stereotypes, others observed behaviors that did not align with stereotypes. Avatar embodiment may also help operators assert authority safely due to being physically hidden from the customers.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".