因事成制：关于中国新闻法制建设的思考

在我国，新世纪以来几件为公众周知的事件促生了一些人所称道的法规和案例。“因事成制”的思维和路径契合我国文化传统，是“因势成事”的具体表现。“因事成制”可以创新制度，也可以进行法律移植。“因事成制”有其存在的现实必要性和可能性，既体现了政治家使新闻传播权利“软着陆”的智慧，又有着深厚的法学理据，论题学法学、伯克利学派，以及哈耶克的有关理论等皆可使其得以证立。“因事”包括“借事”与“造事”，新闻法制变革借事成制已有，而造事未见。     

Two strategies for building a personal vote: Personalized representation in the UK and Denmark

Across parliamentary democracies, elected representatives constitute the link between citizens and government. MPs can connect with voters via the party label, or through personalized forms of representation, which is seen to be increasing in importance. However, scholars disagree on what explains variation in MPs' use of personalized representation strategies. In this article, we argue that politicians use different strategies to personalize the link between themselves and citizens: a constituency-oriented and a person-oriented strategy. To test our argument, we develop a new and novel dataset with behavioral measures of personalized representation. Using a content analyses of 698 British and Danish MPs’ personal websites, we demonstrate that the use of personalization strategies is conditional on the incentives MPs face in terms of electoral insecurity, candidate selection procedures, and the electoral context of the system. Our findings show that the level and type of personalized politics vary across political systems and may pose different types of challenges to party democracies.     

中西文明互鉴中的三种知识汇通模式

明末清初耶稣会士来华所引发的知识汇通,是中西文明互鉴的客观需求和重要表现。中西文化交流过程中形成了三种知识汇通模式:“中学为本”“西学为本”和“中西学并举”。它们分别以“西学东源”“中学西源”和“中西学异源”学说为理论依据,在不同历史时期都曾发挥了主流作用。在中西文化交流频繁的当下,如何将中西知识实现恰如其分的对接,仍是一个十分现实的问题。明末清初以来形成的三种中西知识汇通模式或可提供一些借鉴。     

Deconstructing competing courtroom narratives: representation of social actors

Underpinned by the assumption that the import of the facts and the law in the courtroom depends on how they are invoked and (re)presented mainly through language use by courtroom interactants, this functional linguistic study critically scrutinizes representation strategies that opposing lawyers use to position social actors in their narratives, integrating Halliday’s concept of transitivity and van Leeuwen’s inventory of social actor representation. The findings reveal that the two sides consistently exhibit contrasting discursive practices, and that referential choices and grammatical positioning constitute major semiotic resources that work in concert to construct different identities and alternate realities, and negotiate interpretations of the guilt and innocence of the defendant and the victims. Social actor representation, it is argued, constitutes an important contingency bearing on the outcome of this institutional discourse. Such resources are deeply intertwined with each presenter’s underlying ideologies in this institutional discourse.     

论ICSID仲裁庭对公平公正待遇标准的发展——兼评AES Summit、AES-Tisza诉Hungary案

近年来,国际投资争端中的公平公正待遇标准在ICSID仲裁庭的实践中有了一定的发展。文章首先按时间顺序分阶段梳理该仲裁庭对公平公正待遇标准的界定,随后结合最新的裁决实践,评价公平公正待遇标准要素的发展现状。在此基础上,试图寻找仲裁庭对公平公正待遇标准要素的发展特征。在把握公平公正待遇标准内涵发展趋势的前提下,我们应积极思考并研究我国及投资者的相应对策。     

努力培养适应农村工作的大学生村官

实现大学生村官和农村工作的无缝对接,需要从源头上提高大学生对新农村建设的适应性,也就是要重视高校母体对大学生村官素质的培育工作。为此,要加强思想教育,培养大学生立志建设农村的情感;设置就业指导课,使大学生通过课堂了解党和国家的农业农村政策;通过暑期社会实践活动,使大学生实地感受农村生活;建立大学生与农村农民联系制度,促进大学生岗前转型;将农民代表请进课堂,使大学生不断加深对农村的了解。     

Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naïve Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds >0.95 and >0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp >0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp >0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.     

Why are the affluent better represented around the world?

Scholars have discovered remarkable inequalities in who gets represented in electoral democracies. Around the world, the preferences of the rich tend to be better represented than those of the less well-off. In this paper, we use the most comprehensive comparative dataset of unequal representation available to answer why the poor are underrepresented. By leveraging variation over time and across countries, we study which factors explain why representation is more unequal in some places than in others. We compile a number of covariates examined in previous studies and use machine learning to describe which mechanisms best explain the data. Globally, we find that economic conditions and good governance are most important in determining the extent of unequal representation, and we find little support for hypotheses related to political institutions, interest groups or political behaviour, such as turnout. These results provide the first broadly comparative explanations for unequal representation.     

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.     

建设马克思主义学习型政党的历史透视

中国共产党始终注重学习马克思主义理论,并不断将其与中国实际相结合,努力实现马克思主义中国化。党学习马克思主义理论,在党创立后的前30年,着重从思想上建设党,确立了正确的思想和政治路线,实现了马克思主义中国化第一次历史性飞跃,取得了新民主主义革命伟大胜利;在第二个＂30年＂,对适合中国情况的社会主义建设道路进行了艰苦的探索,教训十分深刻;在最近的＂30年＂,党从号召学习马克思主义理论走向建设马克思主义学习型政党,期间现实了马克思主义中国化第二次历史性飞跃,从而取得了中国特色社会主义事业的伟大成就。党建设马克思主义学习型政党的历史给予我们许多重要启示。