英文摘要 |
Since the 1990s, policy learning between governments to solve policy problems or improve performance has become a critical topic. In 2003, Taipei County (renamed New Taipei City in 2010) followed the policy of the mounted police in the United States and Canada to establish a mounted police force , extending the reach of police services to local tourism. The police departments in Kaohsiung City, Changhua County, and Taichung City also established mounted units to enhance their competitiveness. However, a closer look at the four local governments that established mounted police forces produced different policy outcomes over time. To understand this situation, this study first constructs an analytical framework from the discussion of policy learning theory and collects data using in-depth interviews with decision-makers and executives. To expand the breadth of the research, it refers to the success of the New York and Royal Canadian Mounted Police to identify key factors that trigger policy learning and affect the continuation of the policy. This article finds that in terms of the effect of policy learning, the influence of the knowledge of mayors of local government and commissioners of police departments play critical roles in the transmission of policies. Therefore, a national policy knowledge and learning platform can be established to store and share policy knowledge and continuous learning effects. This makes policy learning will not be changed due to such as elections, developments and transfers, and reduce practice ideological factors. In addition, due to the lack of marketing for civil servants, professional cross disciplinary and multi-disciplinary learning and the use of learning outcomes from different perspectives and experiences can also help provide information for the decision-makers, and enable policies to adapt to changing contexts such as the environment and politics. Finally, encouraging self-learning, ensuring accountability, with social supervision, and through positive competition to improve the effectiveness of policy learning from various fields, will increase the opportunity of the succession of learned policies. |