我国省级数字档案政策文本量化评价研究——基于PMC指数模型

Quantitative Evaluation of Provincial Digital Archival Policy Text in China:Based on the PMC Index Model

【目的/意义】通过建立完善的数字档案政策评价体系,为政府部门制定科学合理的数字档案政策提供参考依据,促进数字档案事业的发展。【方法/过程】利用Python文本挖掘技术,基于PMC指数模型构建数字档案政策文本评价指标体系,对我国76份省级数字档案政策的文本内容进行量化评价与分析。【结果/结论】研究发现,我国大多数省级数字档案政策具备一定的科学合理性,67%以上的政策达到了良好及以上等级。然而,在不同地区的政策中存在时效缺乏综合考虑、政策工具运用失衡、政策对象适用单一的问题。为此,提出了加强制定长短时期相结合的政策规划、充分协调政策工具使用平衡、健全多主体协同联动机制的政策建议。【创新/局限】采用Python文本挖掘技术与PMC指数模型,结合定量分析方法对省级数字档案政策文本进行了全面深入的评价和分析。未来研究将进一步扩大数据来源和研究范围,提高数字档案政策评价的科学性和实用性。

【Purpose/significance】By establishing a sound digital archives policy evaluation system,it provides references for government departments to formulate scientific and reasonable digital archives policies,and promotes the development of digital archives.【Method/process】Using Python text mining technology,based on the PMC index model to build a digital archives policy text evaluation index system,the text content of 76 provincial digital archives policies in my country was quantitatively evaluated and analyzed.【Result/conclusion】The study found that most of the provincial digital archives policies in my country have certain scientific rationality,and more than 67%of the policies have reached the level of good or above.However,in the policies of different regions,there are problems such as lack of comprehensive consideration of timeliness,unbalanced application of policy tools,and single application of policy objects.To this end,policy suggestions are put forward to strengthen the formulation of long-term and short-term policy planning,fully coordinate the balance of policy tools,and improve the multi-subject coordination mechanism.【Innovation/limitation】Using Python text mining technology and PMC index model,combined with quantitative analysis methods,a comprehensive and in-depth evaluation and analysis of the provincial digital archives policy text is carried out.Future research will further expand data sources and research scope,and improve the scientificity and practicality of digital archives policy evaluation.