“一带一路”学术人才向中国流动的开放式“推-拉”模型研究——人工智能方法的运用

To realize the “people-to-people bond” goal in “the Belt and Road” initiate, the key measure is to promote the benign academic faculty mobility. However, the study on the pattern of academic faculty mobility along “the Belt and Road” is rather scarce in this arena. Thus, based on altering the classic “push-pull” theoretical model, this project proposes the “hypothesis of bidirectional interaction between positive and negative forces” in push-pull theory, and introduces an “original model” by referring to researches on academic faculty mobility. Meanwhile, this project aims to examine the mobility intention, calculate the conversion rate from mobility intention to real action, and identify the key variable which mediates the relation between mobility intention and action, of faculty mobility. All of above major goals are at first in China. To realize them, this project plans to 1) develop the artificial intelligence natural semantic analysis robot to effectively capture the distribution of faculty mobility along “the Belt and Road” based on data from Facebook; 2) and build the “ought to be” model based on natural semantic analysis of mobility intention, and the “in fact to be” model based on tracking mobility actions. To sum up, this project applies artificial intelligence methodology in this arena for the first time, and examine the mobility pattern of academic faculty from “the Belt and Road” to China and other part of the world dynamically and differentially. The “open push-pull” model propose in this project will not only provide theoretical foundation and reference for future studies, but also serve to make “the Belt and Road” related policies and initiatives directly.

“一带一路”“民心相通”的关键抓手是促进学术人才的良性流动，但当前学界关于“一带一路”学术人才流动规律的探究十分缺乏。本项目对经典“推拉”理论模型进行改造，提出推拉理论的“正负双向互动关系假设”，并将学术人才流动研究成果引入“推拉”模型，综合形成研究的“原始模型”。创新性开发人工智能自然语义分析机器人，有效甄别Facebook社交平台“一带一路”学术人才分布，基于流动意向的自然语义分析形成应然层面的开放式“推拉”模型，基于流动行动追踪形成实然层面的开放式“推拉”模型，通过对比应然与实然模型，首次完成了学术人才流动意向、流动行动的转化率计算，首次实现了流动意向向流动行动转化的关键中介变量提取。本项目是人工智能方法首次在本领域的应用，将动态化、差异化呈现“一带一路”学术人才向中国和世界其他地区的流动规律，形成的开放式“推拉”模型将为学界后续研究提供理论借鉴，也将直接服务于“一带一路”政策决策。

“一带一路”“民心相通”的关键抓手是促进学术人才的良性流动，但当前学界关于“一带一路”学术人才流动规律的探究十分缺乏。本项目对经典“推拉”理论模型进行改造，提出推拉理论的“正负双向互动关系假设”，并将学术人才流动研究成果引入“推拉”模型，综合形成研究的“原始模型”。创新性构建和使用 “一带一路”学术人才简历大数据库、中国学术人才简历大数据库、全球学术人才简历大数据库、全球学术投稿大数据库、来华留学生大数据库（CUCAS），深入考察“一带一路”学术人才全球流动、对华流动规律。本项目是人工智能方法首次在本领域的应用，将动态化、差异化呈现“一带一路”学术人才向中国和世界其他地区的流动规律，形成的开放式“推拉”模型将为学界后续研究提供理论借鉴，也将直接服务于“一带一路”政策决策。本项目是全球首例“一带一路”学术人才流动规律的科学化研究，首次绘制了沿线国家与中国、沿线国家与欧美主流学术市场、沿线国家内部的学术人才流动图谱，系统呈现了“一带一路”学术人才流动的推拉要素和受力模型。本项目圆满完成，共出版学术专著9部（已出版6部，送交出版社待出版3部），出版中英文学术论文80余篇（另有近10篇英文论文进入审稿阶段），多篇文章被《人大复印资料》全文转载，成功举办5届“一带一路”高等教育研究国际会议，建设多项“一带一路”数据库。课题组还充分发挥智库作用，与教育部教育管理信息中心等部门合作，有效服务国内外“一带一路”高等教育实践。本项目研究成果已获得山东省教学成果特等奖（与中国海洋大学联合），进入国家级教学成果奖申报阶段。