A Study on Graded Evaluation of Civic Scientific Literacy:Based on the Data of the 13th National Civic Scientific Literacy Sampling Survey
The traditional binary assessment method for determining scientific literacy is overly simplistic and does not adequately reflect the development characteristics of scientific literacy across various demographics.Therefore,there is a need to further expand the evaluation methods for scientific literacy.This paper is based on data from the 13th National Civic Scientific Literacy Sampling Survey in 2023 and references international graded evaluation methods to conduct a study on the graded evaluation of scientific literacy among citizens.By combining the main principles of international assessment level divisions with statistical distributions of survey results,citizens are categorized into four levels:possessing high-level scientific literacy,possessing scientific literacy,possessing basic scientific literacy and possessing minimal scientific literacy.The study analyzes the hierarchical differences and structural development characteristics between these different levels of scientific literacy.The relationship between graded scientific literacy and factors such as education,economy,and scientific technological innovation capability is explored through regression analysis.The results indicate that educational factors significantly positively impact individuals with scientific literacy and those with high-level scientific literacy.Furthermore,individuals with scientific literacy and high-level scientific literacy positively influence economic development and innovation.In conclusion,binary classification and graded evaluation of scientific literacy are complementary.While binary classification is straightforward and directly corresponds to scientific literacy,graded evaluation,as an extension of binary classification,can further delineate the distribution of scientific literacy among different populations,providing a more comprehensive reflection of their structural development characteristics.
NETL
NSTL
万方数据
