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A study on day-of-week effect of submission: Based on the data of JSFST

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  • Liu, Tianhao

Abstract

This paper aims at exploring the ways of artificial intelligence to identify and utilize the big data of journals, and adjusting the ways to review journal manuscripts by the collaborative management model of artificial intelligence editing and traditional editing, in an effort to improve the efficiency of initial evaluation. By studying the behavior of authors submitting a paper to Journal of Sichuan Forestry Science and Technology (JSFST), the author tries to find out any unforced regularity in the submission behavior and the relationship between the quality of articles. The data of papers submitted to the submission system of JSFST from November 28, 2017 to November 28, 2019 (i.e. over 2 years, totaling 731 days) were analyzed, with chi-square test available to maintain the validity of the findings. In the context of entropy, the thermodynamic relation is proposed, and the entropy distance is used to measure the disorder of the submission process. The numbers and percentages of accepted and rejected papers as well as the ratio of accepted to rejected papers are compared, and the submission day is checked. The author proposes a new review strategy with particular emphasis on weekly submission day, thus improving effectively the efficiency of initial evaluation. As a new data utilization pattern of deep learning with big data by artificial intelligence, this strategy provides a new way for editors to rapidly screen articles in the era of big data and artificial intelligence, which will inject more vitality into the publishing process and make it easier to find high-quality articles.

Suggested Citation

  • Liu, Tianhao, 2021. "A study on day-of-week effect of submission: Based on the data of JSFST," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    • Handle: RePEc:eee:phsmap:v:563:y:2021:i:c:s0378437120307792
    DOI: 10.1016/j.physa.2020.125470
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