中国现代政治意识建构中的工具理性建设

中国传统政治意识在遭遇近代西学之后逐渐趋于支离破碎。伴随着传统政治意识的碎片化,中国现代政治意识开始重新建构。然而,现代政治意识的建构在中国不仅一波三折,而且其内部结构也不平衡,价值理性明显地占有优势地位,而工具理性则不够健全。工具理性不够健全,一方面导致了形式、程序等在政治生活中的残缺,形式与程序落后于现实政治的需要;另一方面又表现为公民理性的不足,公民既缺乏充分的认知理性,也缺乏必要的行为理性。充实和健全现代政治意识工具理性迫切地要求政治学特别是政治科学既要充分吸收西方现代政治科学的方法、范式、结论等优秀成果,更要加速其本土化与普及化的进程,发展中国特色的政治认知理性与实践理性。     

论法治实施的技术手段——法律推理

根据法律推理定义的演变和发展,给出了符合当今法治社会特点的法律推理定义,阐述了还原推理、衡平推理、类比推理和辩证推理,探讨了法律推理的客观性评价标准,总结了法律推理应遵循的哲学原则和在司法实践中应遵循的原则.在多元化价值取向的当今社会,通过法律推理,可以达到法律生活各种层面的法治目标.     

论逻辑图表与逻辑教育

逻辑教育已成为一项至关重要的公民素质教育,但当前的逻辑教育效果并不如人意。本文指出,逻辑图表能帮助人们较轻松和直观地理解和应用逻辑推理,而存在图作为一种图表化理论,对逻辑教育有着突出的合目的性和效用性。     

论中国古代刑法典中的概括性禁律——以《大清律例》为例

中国古代成文刑法典中的概括性禁律源远流长。在中国的传统语境中,概括性禁律的产生具有必然性。借助于司法过程的展开,概括性禁律不仅发挥着在疑难案件的审谳中建构法律推理大前提的功能,而且对礼教秩序和皇权具有补充性维护作用,并为司法擅断提供“合法性”依据。     

The minimum discrimination information approach in analyzing categorical data

A brief review of the minimum discrimination information (MDI) approach in analyzing categorical data is presented in a question -answer format, An example is given to bring out situations in which the MDI approach is more useful. No new results are proved.     

Comparing time trends in a two-arm longitudinal study with an ordinal categorical response

A within-subject monotonicity index is proposed to summarize the fol-lowup for each subject in a longitudinal study where the response is required to be only ordinal. The individual measures can be weighted to detect different trend behaviors of interest. Asymptotically normal tests for single group alternatives or for comparing the means of two groups are derived and illustrated with clinical data.     

The analysis of ordinal time-series data via a transition (Markov) model

While standard techniques are available for the analysis of time-series (longitudinal) data, and for ordinal (rating) data, not much is available for the combination of the two, at least in a readily-usable form. However, this data type is common place in the natural and health sciences where repeated ratings are recorded on the same subject. To analyse these data, this paper considers a transition (Markov) model where the rating of a subject at one time depends explicitly on the observed rating at the previous point of time by incorporating the previous rating as a predictor variable. Complications arise with adequate handling of data at the first observation (t=1), as there is no prior observation to use as a predictor. To overcome this, it is postulated the existence of a rating at time t=0; however it is treated as ‘missing data’ and the expectation–maximisation algorithm used to accommodate this. The particular benefits of this method are shown for shorter time series.     

Bayesian methods for contingency tables using Gibbs sampling

Cell counts in contingency tables can be smoothed using loglinear models. Recently, sampling-based methods such as Markov chain Monte Carlo (MCMC) have been introduced, making it possible to sample from posterior distributions. The novelty of the approach presented here is that all conditional distributions can be specified directly, so that straight-forward Gibbs sampling is possible. Thus, the model is constructed in a way that makes burn-in and checking convergence a relatively minor issue. The emphasis of this paper is on smoothing cell counts in contingency tables, and not so much on estimation of regression parameters. Therefore, the prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage, and a vague prior for hyperparameters at the second stage. The smoothed counts tend to compromise between the observed data and a log-linear model. The methods are demonstrated with a sparse data table taken from a multi-center clinical trial. The research for the first author was supported by Brain Pool program of the Korean Federation of Science and Technology Societies. The research for the second author was partially supported by KOSEF through Statistical Research Center for Complex Systems at Seoul National University.     

On identifiability of parametric statistical models

Summary This is a review article on statistical identifiability. Besides the definition of the main concepts, we deal with several questions relevant to the statistician: parallelism between parametric identifiability and sample sufficiency; relationship of identifiability with measures of sample information and with the inferential concept of estimability; several strategies of making inferences in unidentifiable models with emphasis on the distinct behaviour of the classical and Bayesian approaches. The concepts, ideas and methods discussed are illustrated with simple examples of statistical models. Centro de Análise e Processamento de Sinais da UTL     

论充足理由律在法律推理中的作用

在审判过程中,法官的法律推理活动总的表现为以法律规范为大前提,以确认的案件事实为小前提, 前通过二者的联结进而得出结论的演绎论证模式。该模式表明的只是审判活动的总体思维结构,其 中,特别是对案件事实的确认,即小前提的建立,和在此基础上“寻找”或援用可适用于此案件事实的 法律依据,即大前提的建立,都包含了比较复杂的思维活动,涉及到许多逻辑问题。其中逻辑思维规 律中的充足理由律在法律推理过程中的作用尤为突出。