The shape of beauty: determinants of female physical attractiveness
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».