Development and validation of a measure of attitudes towards sex doll ownership
Bibliographic record
Abstract
Sex dolls are beginning to become more mainstream, both in the public’s consciousness and in academic research. However, there is no current systematic examination of public attitudes towards sex dolls within the peer-reviewed literature, which represents a barrier to the efficient study of this topic. In this paper, we report the development and validation of such a measure. Using an international public sample, we found that Sex Doll Ownership Attitudes Scale (SDOAS) was underpinned by three factors: ‘Acceptability of Doll Ownership’, ‘Doll Owners as Immoral’, and ‘Doll Owners as Dysfunctional’ (Study 1; N = 377). Scores on each of these factors were predicted by participant sex, religiosity, permissive sexual attitudes, right-wing authoritarianism, and moral intuitions that favour personal liberty. This structure was confirmed in an independent UK sample, where links to policy support related to sex doll ownership were also established (Study 2; N = 329). We also demonstrated how the SDOAS can be used as an outcome measure when investigating views about dolls with different appearances and functions (Study 3; N = 292). We argue that the SDOAS possesses strong psychometric properties, and is suitable for use in different research designs in this emerging field of study.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".