Regulation of armed conflict: critical comparativism

Abstract

This paper calls for comparative analysis of international humanitarian law and Islamic laws regulating armed conflict by focusing on the underlying assumptions and interests informing both systems (rather than on rule-based comparison). It argues that examination of the biases inherent to each legal system can potentially inform scholars to understand better the paradigms shaping each of them. In doing so, the paper builds on contextual and critical interpretations of both fields of law to assert the need for ‘critical comparativism’ rather than functionalist comparativism. Unlike functionalist comparativism, which treats international law as the ‘objective’ benchmark against which other legal traditions are measured, ‘critical comparativism’ treats the two legal systems examined as alternative manifestations of power structures which, when contrasted against each other, help shed more light on the inherent bias in each legal system.     

Patella sex determination by 3D statistical shape models and nonlinear classifiers

Sex determination is one of the essential steps in personal identification of an individual from skeletal remains. Most elements of the skeleton have been subjected to discriminant function analysis for sex estimation, but little work has been done in terms of the patella. This paper proposes a new sex determination method from the patella using a novel automated feature extraction technique. A dataset of 228 patellae (95 females and 133 males) was amassed from the William M. Bass Donated Skeletal Collection from the University of Tennessee and was subjected to noninvasive high resolution computed tomography (CT). After the CT data were segmented, a set of features was automatically extracted, normalized, and ranked. The segmentation process with surface smoothing minimizes the noise from enthesophytes and ultimately allows our methods to distinguish variations in patellar morphology. These features include geometric features, moments, principal axes, and principal components. A feature vector of dimension 45 for each subject was then constructed. A set of statistical and supervised neural network classification methods were used to classify the sex of the patellar feature vectors. Nonlinear classifiers such as neural networks have been used in previous research to analyze several medical diagnosis problems, including quantitative tissue characterization and automated chromosome classification. In this paper, different classification methods were compared. Classification success ranged from 83.77% average classification rate using labels from a Fuzzy C-Means (FCM) clustering step, to 90.3% for linear discriminant classification (LDC). We obtained results of 96.02% and 93.51% training and testing classification rates, respectively, using feed-forward backpropagation neural networks (NN). These promising results using newly developed features and the application of nonlinear classifiers encourage the usage of these methods in forensic anthropology for identifying the sex of an individual from incomplete skeletons retaining at least one patella.