略论20世纪法国人类学思想的嬗变

肇始于迪尔凯姆学派的法国人类学在20世纪取得了辉煌的成就.其思想发展进程可概述如下:首先,马塞尔·莫斯和赫兹提出了"二元分类"思想;其后,列维-斯特劳斯在广泛吸收、综合社会学、人类学和语言学等学科思想的基础上,以"二元对立"的认知结构为核心,建构了结构主义人类学.所有这些成果,对皮埃尔·布迪厄在20世纪60年代的人类学研究产生了重要影响.     

教育价值研究三十年

教育价值研究三十年来发生了两大变化:一是从理论概念辨析走向实践问题批判,二是致力于全球视野与本土行动。国内研究者主要从教育价值的概念、教育价值分类、教育价值观、教育价值取向等方面对教育价值进行了探究,并取得了相应的成果。今后的教育价值研究在内涵上应更加强调精神和意义的"深度",分类上应坚持理论与实践的适度分离,价值观上应反思多元化时代的价值迷失现象,价值取向上应注重对人的生存论关怀,研究方法上应致力于教育价值研究传统的建构。     

社会环境观在连续体文体分析中的应用——基于《高级英语》的案例分析

用韩礼德的社会环境观分析连续体文体《高级英语》，既可以加深对《高级英语》的认识，又能探索出一条区别于语言描写的连续体文体分析之路。分析过程表明：社会环境观的研究角度独特，研究结果见地深刻，且操作性强，但与语言描写法相比，其产生的结论较为宏观，故在日常的研究中，两种方法应结合起来使用，以相辅相成，互作补充。     

论公司治理模式类型的划分

关于公司治理模式,目前存在着各种各样的分类方法.这些方法对于认识和比较各种公司治理模式的长短得失是很有意义的,但它们又不同程度地存在着一些问题.在现代公司中,由于管理者掌握着公司的实际控制权,因而有必要以管理者所受约束为标准划分公司治理模式的类型.考虑到管理者所受约束的实际情况,可将公司治理模式分为市场约束型、关系约束型、政府约束型和家族约束型等四种类型.     

Girlz II women: Age‐grading,language change and stylistic variation

Informed by abstract models of language change or stability over time, we present a longitudinal study of two African American females, first interviewed as teenagers, and re‐recorded twenty years later. As teenagers, they used morpho‐syntactic features of AAVE voraciously. But as working adults, these women distance themselves from their teenage activities and social networks, and display a considerably reduced vernacular usage that accords with their articulated concern to get ahead. The diachronic interpretation that best characterizes their transformation is age‐grading rather than generational change, since change at the individual level is accompanied by stability at the community level. The picture is complicated by intermediate recordings showing that one of the speakers is a stylistic chameleon, capable since her teenage years of varying copula absence rates depending on addressee, topic, and projected persona. But the age‐grading interpretation of change at the individual level remains valid based on the evidence of her reduced use of habitual be2, and third singular present tense –s absence. The case highlights the importance of paying more attention to stylistic variation and including more than two time points in sociolinguistic studies of change in real and apparent time.     

The classification accuracy of four problem gambling assessment instruments in population research

Improved methodology was used to re-examine the weak correspondence between problem and pathological gamblers identified in population surveys and subsequent classification of these individuals in clinical interviews. The SOGS-R, the CPGI, the NODS and the Problem and Pathological Gambling Measure (PPGM), as well as questions about gambling participation and expenditures, were administered to a total of 7272 adults. Two clinicians then assessed each person's status, based on comprehensive written profiles derived from these questionnaire responses. Instrument classification was then compared to clinical classification. All four instruments correctly classified most non-problem gamblers (i.e. had good to excellent sensitivity, specificity and negative predictive power). However, the PPGM was the only instrument with good classification of problem gamblers (i.e. excellent sensitivity and positive predictive power). The CPGI and SOGS-R had weak positive predictive power and the NODS had only adequate sensitivity and positive predictive power. Improvement in the classification accuracy of the CPGI occurred when a 5+ cut-off was used and when a 4+ cut-off was used with the SOGS. In general, the classification accuracy of the NODS, SOGS and CPGI is better than prior research suggested but overall accuracy is still modest. With adjusted cut-offs, all three instruments are reasonably congruent with clinical ratings.     

Model-Based Classification via Mixtures of Multivariate t-Factor Analyzers

A model-based classification technique is developed, based on mixtures of multivariate t-factor analyzers. Specifically, two related mixture models are developed and their classification efficacy studied. An AECM algorithm is used for parameter estimation, and convergence of these algorithms is determined using Aitken's acceleration. Two different techniques are proposed for model selection: the BIC and the ICL. Our classification technique is applied to data on red wine samples from Italy and to fatty acid measurements on Italian olive oils. These results are discussed and compared to more established classification techniques; under this comparison, our mixture models give excellent classification performance.     

分级培养模式下有效教学探析

本文探讨了大众化教育阶段高校实施分级教学的教育学和心理学理论依据，指出了实施分级教学的重要性和必要性，提出了分级教学应遵循的五个原则。阐述了有效教学的教学理念和含义。并以某大学为例，论述了在分级培养模式下进行高等数学有效教学的教学情境设计与实施方法。     

论刑事证据的法律分类

本文对刑事证据的“种类”、“分类”之称谓进行辨析,认为两者实质含义一致,进而提出采 用“法律上的分类”与“理论上的分类”的观点。作者立足我国刑事证据的立法分类,分析其规定不足, 建议重新分类,特别指出刑事证据分类应注视一、二审的转化问题和与今后将建立的证据规则相衔接 问题。     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.