目前高校社科系,所资料室,特别是综合性的社科系、所,对于资料的分类,大部分在沿用五、六十年代的分类方式和手段。有的只按几大学科归类;有的则在各学科内辟有不同层次的专题分类;也有的仅从资料载体不同的角度加以区分。而完成这样几种分类方式的手段:一是插标立档,从占用空间上给予区分;二是将各条资料编录成题录卡片(或称篇目卡片),建立成箱、成柜的卡片索引。这些分类方式是多年来资料人员在工作实践中摸索形成的一种传统方式,从未有过统一规范或要求,在过去相当长的时间里基本上能够满足或适应读者需要。但是,随着形势的发展,科技的飞跃,近些年来这种分类方式逐渐显露了它的缺陷与不足。第一,传统的资料分类范围难以包括社会科学领域内新兴学科的内容。七、八十年代以来,社会科学领域内边缘学科、交叉学科和新兴学科逐渐形成体系、成为独立的学科。有的已进入高校课堂或成为专家学者们攻关的重点科研课题。在这种形势下,按照原有的分类框架进行资料分类,就会使本来很有价值的资料因得不到确切的分类而被忽略。第二,传统 At present, colleges and universities department of social sciences, the reference room, especially a comprehensive social science department, for the classification of data, most of the follow the fifties and sixties classification and means. Some are classified according to several university subjects only; others are classified into different levels in different disciplines; others are only differentiated from different perspectives of data carriers. The completion of such means of classification of several ways: First, plug the file, from the space to give distinction; the second is to catalog all the information into a bibliographic card (or title card), built into boxes, into a counter card index. These classification methods are a traditional way for information workers to work through the years of practice. They have never had a unified standard or requirement and have basically been able to meet or adapt to readers’ needs for quite a long time. However, with the development of the situation and the leap in science and technology, this classification has gradually revealed its flaws and shortcomings in recent years. First, it is difficult to include the content of emerging disciplines in the field of social science with the traditional data classification. Since the 1970s and 1980s, the marginal disciplines, interdisciplinary and emerging disciplines in the social sciences have gradually become systems and become independent disciplines. Some have entered the university classroom or become the key scientific research topics that experts and scholars tackle. Under such circumstances, the classification of data according to the original classification framework will result in the omission of the originally valuable data due to lack of exact classification. Second, tradition