Sex and ancestry estimation methods in modern Filipino crania (Mga paraang pagsusuri ng kasarian at lahi mula sa bungo ng mga kasalukuyang Pilipino)
Notice bibliographique
Résumé
Diversity has been a central focus within anthropology since its disciplinary origins. In forensic anthropology this has come to include understanding the wide range of physical variation present in the human species across the spectra of geographies, generations, life stages, sexes, and different lived experiences for the purposes of estimating group membership and identification. Research has particularly flourished in the Americas, Europe, South Africa, and Australia largely owing to a history of prominent scholars, well-equipped university graduate programs and facilities, and large skeletal reference collections and databases that characterize these regions. Relative to these areas and the populations studied therein, East and Southeast Asia have received less scholarly attention. This is surprising given that the diversity found in these regions represents a substantial portion of both worldwide population and variation and that these regions are home to many forensically significant (i.e., vulnerable) groups. Filipinos, whom in particular have received little to no attention, are brought to focus here given the convergence of demographic, geographic, and historical factors that greatly contribute to the need for anthropological identification of human remains from this population. The current study ameliorates this problematic research gap by: (1) exploring methods of metric and nonmetric Asian sex and ancestry estimation that incorporate modern Filipino samples, specifically concentrating on the cranium, and (2) bolstering collaborative research capacities through the creation of a novel and internationally accessible Filipino reference collection from skeletons in the Philippines. The three methods explored include: (1) the optimized summed scored attributes (OSSA) method for sex estimation, (2) discriminant function analysis (DFA) via the Fordisc 3.1 (FD3) software for ancestry estimation, and (3) multivariate probit regression (MPR) for ancestry estimation.\nFirst, the OSSA method originally intended for use in ancestry estimation was appropriated to test the applicability of the method for sex estimation using five cranial traits given the methodological similarities between classifying sex and ancestry. A large sample of documented crania from Japan and Thailand (n = 744 males, 320 females) are used to develop a heuristically selected OSSA sectioning point of ≤1 separating males and females. This sectioning point is validated using a holdout sample of Japanese, Thai, and Filipino (n = 178 males, 82 females) individuals. The results indicate a general correct classification rate of 82% using all five traits, and 81% when excluding the mental eminence.\nSecond, ancestry classification trends of the Filipino sample (n = 110) were analyzed when using craniometric measurements and DFA via FD3. Results show the greatest classification into Asian reference groups (72.7%), followed by Hispanic (12.7%), Indigenous American (7.3%), African (4.5%), and European (2.7%) groups included in FD3. This general pattern did not change between males and females. Moreover, replacing the raw craniometric values with their shape variables did not significantly alter the trends already observed.\nThird, MPR models were used to classify the ancestral affiliation of Filipino crania using morphoscopic traits. The overall correct classification rates for three-group and four-group models were 72.1% and 68.6%, respectively. Filipinos classified as Asian 52.9% of the time using three ancestral parental groups and 48.6% using four groups. A large portion of Filipinos also classified as African. There were no significant differences in classification trends or accuracy rates between complete crania and crania with at least one missing variable.\nMuch as this work emphasizes methodological advancements in Filipino biological profile estimation, it also more broadly attempts to introduce forensic anthropology in and of the Philippines as a discourse worthy of more mainstream study. Both the generation and application of knowledge in forensic anthropology have only begun in the Philippines. The outcome of missing persons investigations is dependent on the scale, infrastructure, and political will of the context. This work hopes to inspire the improvement of all three and provide forensic anthropology in the Philippines its due attention.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».