《元朝秘史》版本流传考

《元朝秘史》是一部重要的蒙古学文献,相关研究已经成为一个国际性的学术领域,长期以来研究成果不计其数,涉及历史、语言、文学、宗教、社会学等诸多领域。其中,文献学方面的研究开始得最早,取得的成绩较大,受关注的程度也较高。然而《元朝秘史》文献学方面的研究仍然存在继续扩展和深入的空间。例如在《元朝秘史》版本流传的考订方面,随着客观环境的逐步改善,某些方面已经有条件在前人的基础上有所前进和完善,有必要对以往的研究做些修正和补充。     

高等学校国防教育学科建设研究

高等学校国防教育学科建设是根据国家开展全民国防教育的方针政策和策略,并结合高校自身开展国防教育的实践及其特点和规律,采取必要的措施和手段,促进高校国防教育学科发展和水平提高的活动过程。本文主要从高等学校国防教育学科建设的界定,高等学校国防教育学科建设的意义及价值,我国高等学校国防教育学科建设取得的主要成就和存在的主要问题,以及推进我国高等学校国防教育学科建设的对策与措施等方面,来阐述对高等学校国防教育学科建设的观点及看法。     

70年致力于民族学研究的学者——西南民族大学陈宗祥先生专访

陈宗祥先生是我国最早介绍、研究《格萨尔》史诗的,学者之一,也是《白狼歌》研究具有影响力的学者之一。他翻译的《格萨尔王传》被认为是最早研究格萨尔的参考资料之一,他提出“白狼语～南语一西夏语一普米语等一脉相承”的重要见解,被认为是对南语研究的一大突破。陈宗祥先生尤其将70年来对民族学乌牛白羊两系的探索,贯穿到他对东汉《白狼歌》部落史、古蜀史的考证和敦煌古藏文《南语写卷》的翻译研究上,独树一帜。学界鲜为人知的是,20世纪40至50年代,陈先生收集和翻译的海门道夫的中印边界东段调查报告,最早向国内介绍了喜马拉雅山麓南侧坡九万平方公里地区的情况。我们有幸对陈宗祥先生进行了独家专访,陈先生首次对自己的人生、学术经历和学术思想做了回顾和总结,是授权公开发表的第一篇访谈文章。     

70年中国新闻史研究述略

中华人民共和国成立70年来,与政治制度变革和政治路线变迁相顺应,前30年的新闻史研究,“革命史范式”占据主导地位,研究队伍人数较少,研究对象局限于无产阶级新闻事业。后40年的新闻史研究,新闻史的学科地位确立,汲取传播学及其他学科理论与方法,“新闻本体范式”兴起,研究范式多样化,中国新闻史学会成立,学术队伍人才济济,学术成果丰硕并走向世界。此后的新闻史研究,须更多负起“资治”“知来”之责,创新研究范式,填补研究空缺,建设中国特色新闻史学。     

王国维“通人之学”与“专人之学”的尺短寸长

王国维“通人之学”“专人之学”等学术思想是中国学术研究转型的重大创新,通过对王国维学术成长的梳理、学术成果的分析、学术思想的论证,对“静安之学”进行了全面系统的研究。研究认为,王国维凭借厚实的知识贮备,批判地继承西方哲学理论与中国古代哲学思想,在构建中国大文科的学科体系、研究文史哲的学术体系和中国学者的学术话语体系方面做出了全面系统实践,取得重大进展和开创性成果,堪称开一代风气的大学者;王国维以创辟胜解、通方知类的学术思辨,创立二重证据方法,探索出一条由“通人之学”到“专人之学”学问研究蹊径。系统审读、反思静安之学,对我们严肃读书风气、匡正学问研究态度、提倡学术争鸣、甄别解决知识产权争端、清除学术造假现象、规范学术研究流程,能够起到见贤思齐、弃旧图新之作用。     

A Statistical Issue About a Phase III Multi-regional Trial for the Survival Data under Accelerated Failure Time Model with Unrecognized Heterogeneity

For the time-to-event outcome, current methods for sample size determination are based on the proportional hazard model. However, if the proportionality assumption fails to capture the relationship between the hazard time and covariates, the proportional hazard model is not suitable to analyze survival data. The accelerated failure time model is an alternative method to deal with survival data. In this article, we address the issue that the relationship between the hazard time and the treatment effect is satisfied with the accelerated failure time model to design a multi-regional trial for a phase III clinical trial. The log-rank test is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is used to determine the total sample size for a multi-regional trial and the consistent trend is used to rationalize partition sample size to each region.     

Sensitivity analysis for two control groups

In many experiments where data have been collected at two points in time (pre-treatment and post-treatment), investigators wish to determine if there is a difference between two treatment groups. In recent years it has been proposed that an appropriate statistical analysis to determine if treatment differences exist is to use the post-treatment values as the primary comparison variables and the pre-treatment values as covariates. When there are several outcome variables, we propose new tests based on residuals as alternatives to existing methods and investigate how the powers of the new and existing tests are affected by various choices of covariates. The limiting distribution of the test statistic of the new test based on residuals is given. Monte Carlo simulations are employed in the power comparisons.     

A Meta Analytic Approach to Testing for Panel Cointegration

We propose new tests for panel cointegration by extending the panel unit root tests of Choi (2001 Choi , I. ( 2001 ). Unit root tests for panel data . Journal of International Money and Finance 20 ( 2 ): 249 – 272 .[Crossref], [Web of Science ®] , [Google Scholar]) and Maddala and Wu (1999 Maddala , G. , Wu , S. ( 1999 ). A comparative study of unit root tests with panel data and a new simple test . Oxford Bulletin of Economics and Statistics 61 ( S1 ): 631 – 652 .[Crossref] , [Google Scholar]) to the panel cointegration case. The tests are flexible, intuitively appealing, and relatively easy to compute. We investigate the finite sample behavior in a simulation study. Several variants of the tests compare favorably in terms of both size and power with other widely used panel cointegration tests.     

Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research

Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between treatment groups in observational studies. Standardized differences were initially developed in the context of comparing the mean of continuous variables between two groups. However, in medical research, many baseline covariates are dichotomous. In this article, we explore the utility and interpretation of the standardized difference for comparing the prevalence of dichotomous variables between two groups. We examined the relationship between the standardized difference, and the maximal difference in the prevalence of the binary variable between two groups, the relative risk relating the prevalence of the binary variable in one group compared to the prevalence in the other group, and the phi coefficient for measuring correlation between the treatment group and the binary variable. We found that a standardized difference of 10% (or 0.1) is equivalent to having a phi coefficient of 0.05 (indicating negligible correlation) for the correlation between treatment group and the binary variable.     

Transformed Logit Confidence Intervals for Small Populations in Single Capture–Recapture Estimation

The good performance of logit confidence intervals for the odds ratio with small samples is well known. This is true unless the actual odds ratio is very large. In single capture–recapture estimation the odds ratio is equal to 1 because of the assumption of independence of the samples. Consequently, a transformation of the logit confidence intervals for the odds ratio is proposed in order to estimate the size of a closed population under single capture–recapture estimation. It is found that the transformed logit interval, after adding .5 to each observed count before computation, has actual coverage probabilities near to the nominal level even for small populations and even for capture probabilities near to 0 or 1, which is not guaranteed for the other capture–recapture confidence intervals proposed in statistical literature. Thus, given that the .5 transformed logit interval is very simple to compute and has a good performance, it is appropriate to be implemented by most users of the single capture–recapture method.