Chinese academic journals: quality,issues and solutions

Twelve Chinese academic journals, indexed by SCI and financed by the National Natural Science Foundation (NNSF), were analysed using the 2000–2003 Total Citations and Impact Factor data from JCR. It is concluded that the quality of Chinese journals is generally low and has remained so despite recent government measures aimed at improvement. Several factors are influential – particularly the Chinese government's policy of encouraging submission to foreign journals. Finance, management, and the academic degree policy are also obstacles. It is suggested that the Chinese government should reconsider its current policies so as to encourage higher quality in domestic periodicals and thus a greater recognition of Chinese journals by the international academic community.     

Communicating Prevention: The Effects of the keepin' it REAL Classroom Videotapes and Televised PSAs on Middle-School Students' Substance Use

This study sought to determine if exposure to two communication-oriented activities, videotapes and public service announcements, accounts for changes in substance use among adolescents participating in the Drug Resistance Strategies Project's keepin' it REAL adolescent substance use prevention curriculum. Middle-school students (4,734, 72% Latino) responded to questionnaires related to these analyses. An analysis of covariance (ANCOVA) model was fit separately to six substance use outcomes. The results suggested that intervention students who saw four or five videos engaged in less substance use in the past month than did students who saw fewer videos. Having seen the PSAs one or more times did not predict the reported change in substance use.     

Multilingual modeling of cross-lingual spelling variants

Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV). To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns. We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64–78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.