The shape of beauty: determinants of female physical attractiveness
Bibliographic record
Abstract
Rarely has one research area gained as much attention as that which is observed for female physical attractiveness. The past decade has resulted in numerous, exciting developments, particularly with respect to three proposed determinants of beauty: waist to hip ratio (WHR), body mass index (BMI), and curvaceousness. The goal of our paper is to provide a highly necessary review of contemporary research on the female attractiveness, including an in-depth examination of these factors. In our review, we first discuss WHR, an index of fat deposition, which is calculated by measuring the circumference of the waist compared to the circumference of the hips. WHR is controlled by the sex hormones, and increases as women age, and hence, may influence perceptions of attractiveness. This factor has been hotly contested, as some researchers have claimed that a WHR of approximately 0.7 is universally most attractive, whereas others have found inconsistent findings, or suggest the importance of other factors, such as BMI. Body mass index (BMI), calculated by dividing the body weight (in kilograms) by height (in meters) squared, serves as a measure of body fat. Although WHR and BMI are correlated, they lead to different conclusions, and the importance of BMI as a measure of female attractiveness is debated in the literature. Similar to WHR research, BMI and its role in attractiveness is not cross-culturally consistent and is affected by the availability of resources within a given environment. It may be the case that both WHR and BMI influence female attractiveness. However, there has been little investigation of this possibility. We have explored this issue in our research, which revealed that both influence attractiveness, but in addition, we noticed that curvaceousness was also a factor. Curvaceousness is the degree of "hourglass" shape as determined, for example, by the size of the bust, relative to the circumference of the hips and waist, and the size of the buttocks. However, curvaceousness does not appear to be temporally stable as a marker of attractiveness, and it is not consistent across modes of presentation. For example, models in male-oriented magazines are more curvaceous than models in female-oriented magazines. In summary, faced with these recent findings, it is difficult to ascertain agreement among the various factors, especially when researchers investigate each determinant in isolation. We conclude that, although researchers have made many important initial steps in examining female attractiveness, there remains much to be discovered.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".