Seeking Connection: Affiliation Motives Underlie the Feminization of Products
Bibliographic record
Abstract
“Female” anthropomorphized products are ubiquitous, from voice assistants like “Siri” to humanoid robots like “Sophia,” the first robot granted citizenship in Saudi Arabia. Advocates for equality warn that such gendering arises from problematic gender beliefs; however, we unearth a unique motivation for feminizing anthropomorphized products: desire for social affiliation. Across six independent studies (N = 5,016), we find that a desire for affiliation, above and beyond other motivations (i.e., power) and beliefs (i.e., sexism), underlies people’s gendering of, and choice for, feminized products. In demonstrating the association between desire for affiliation and feminization, we unearth one reason why anthropomorphized technology—technology often meant to fulfil affiliative needs—is gendered as female. We also provide one potential solution to counter it: imbuing products with feminine stereotypes (but not feminine gender). These results speak to the paradox of gendering anthropomorphized products in an increasingly digitized world.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".