晒图征文10号:由文件归类所想到的
从文件归类所想到的日常工作繁杂无比,文件类别纷杂,如何对文件进行有效归类,是我经常思考的问题。文件的归类是指按一定的规律或者特点对文进行收集。高效的文件归类会给工作带来极大的方便,不仅便于查找,提高工作效率,更有利于形成良好的思维模式,养成良好的思维逻辑。因此,看似简单的文件分类收集,实则有着大学问。
究竟如何进行文件归类呢?不同的工作岗位有着不同的细分方法,但大体原则基本一致,最基本的就是,便于自己查找,也便于他人查找。我针对自己的工作特点,基本按时间进行文件收集,先按年度界限收集,在年度范围内按月份进行收集,月度里在进行分类收集。实践证明,利用时间界限分类收集文件这种方法不仅方便文件查找,也大大提高了工作效率。
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
方便查找,提高效率只是文件有效收集所展现的表象功能之一。实际上,高效的文件收集对我们养成良好的思维逻辑也起着至关重要的作用。文件分类收集的方式有很多种,究竟选择哪种?为何选择这种?在收集之前都是要经过深思熟虑的,这样分类是不是更有效、更方便,文件取这个名字是否更一目了然,这些问题都要结合实际工作的进行深度思考,这一过程,充分发挥了逻辑思维功能,充分发挥了思维的主观能动性,一旦养成习惯,将会潜移默化地运用到其他工作处理中去,大大提高处理问题的科学性、合理性。
高效文件收集还有利于进一步细化工作,便于总结提高。文件分类越细,工作也做得越精。但是,文件分类收集工作也不能局限于收集分类。对于同类型的工作,要从不同的文件列出其个性特征,总结出共性特征,形成具有普适性的思维模式,甚至可以作为处理其他工作的指导。**** Hidden Message *****
全部按时间顺序来设置,个人感觉还是不太方便哦,纵横交错为宜。当然,自我感觉适用便好。 老猫老师:在月里面还是按工作类容分类归总的。 一看理论水平就很高,学习! 很有道理,受益匪浅啊
列出个性特征,找到共性特征 日积月累,聚沙垒塔,更上一层楼 日积月累,聚沙垒塔,更上一层楼 条理性很重要目的有三;一是自己能快速找到。二是能将具有共同属性的归类,便于单独系统分析。三是满足各项制度规定。 怎么截图老是打不开呢
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