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題名:當前政治學門之內外陰影與光明展望
書刊名:習慣領域期刊
作者:劉正山 引用關係
作者(外文):Liu, Cheng-shan
出版日期:2017
卷期:8:2
頁次:頁25-45
主題關鍵詞:方法論知識論政治政治學實用主義MethodologyEpistemologyPoliticsPolitical sciencePragmatism
原始連結:連回原系統網址new window
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習慣領域學說不僅能運用在認識個人及組織的限制和潛力,也能運用在更高層次的現象觀察。本研究應用習慣領域學說的深度視野,勾勒出政治學門的習慣領域,指出學門的內外陰影──內有知識論立場分立而找不到相通點的困境,對外則有學生家長乃至社會各界對於嚴重學用落差的質疑,以及如何找到光明展望。政治學門長期受限於對政治的定義以及既有知識論體系的固化,不易找出轉變社會對於政治學刻板印象的方法。本研究提出擴展學門實際領域的兩個倡議:一是在知識論體系上,認清不同知識論體系的強處之後,重新正視社會科學領域長期忽略的實用主義中探討人性的傳統;二是在方法論體系上,重新面對「意義探勘」這個方法論的重要性。前者能縮短學生及社會對於政治學學用落差的認知,後者則能促進學門內及跨學門更高層次的合作。實用主義者的核心關懷是認識自己與他人痛苦的來源並提出解決策略,因此,若能在政治學門中重新恢復實用主義,結合意義探勘的方法論,並重新推出新的課程,不但大有機會讓政治學成為落實習慣領域學說「覺、學、用、享」理念的學門,也足以幫助未來各行各業的領導者迎接數據時代中「人」(治理)與「機」(人工智慧)必須分庭抗禮的局面。
Political science as a discipline has been challenged from within and outside. The division between the major two epistemological stances-positivism and interpretivism -has dominated the development of the discipline, while the society in Taiwan remains doubtful about if this discipline could help build a better government and provide students better skills for job market. This article summarizes the sources of the problems and how likely these serious problems could be resolved. The proposed solution is a synergy of my personal observation about the field, experiences of teaching political methodology, and learning about the habitual domains (HD) theory. I suggest two solutions to solve the above challenges to the discipline: First, take the "thick data" or meaning mining methodological approach to weave researchers from different kingdoms of epistemology. Second, revive the pragmatism tradition that prioritizes humanity and human nature as the subjects of study. These two strategies of re-focusing the discipline will help future statesmen and stateswomen to balance the influence of artificial intelligence and bring wellbeing to their fellow citizens.
期刊論文
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4.Bond, J. R.(2007)。The scientification of the study of politics: Some observations on the behavioral evolution in political science。Journal of Politics,69(4),897-907。  new window
5.Dalton, Craig M.、Taylor, Linnet、Thatcher, Jim(2016)。Critical data studies: A dialog on data and space。Big Data & Society,3(1),1-9。  new window
6.Dezelan, T.、Hafner, D. F.、Melink, M.(2014)。First-job educational and skill match: An empirical investigation of political science graduates in Slovenia。International Journal of Manpower,35(4),553-575。  new window
7.Di Paolo, A.、Mane, F.(2016)。Misusing our talent? Overeducation, overskilling and skill underutilisation among Spanish PhD graduates。Economic and Labour Relations Review,27(4),432-452。  new window
8.Elman, C.、Gerring, J.、Mahoney, J.(2016)。Case study research putting the quant into the qual。Sociological Methods & Research,45(3),375-391。  new window
9.Ferrante, F.(2017)。Great expectations: The unintended consequences of educational choices。Social Indicators Research,131(2),745-767。  new window
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11.Schatz, E.、Maltseva, E.(2012)。Assumed to be universal: The leap from data to knowledge in the American Political Science Review。Polity,44(3),446-472。  new window
12.Paolacci, G.、Chandler, J.(2014)。Inside the turk: Understanding Mechanical Turk as a participant pool。Current Directions in Psychological Science,23(3),184-188。  new window
13.Monroe, B. L.、Pan, J.、Roberts, M. E.、Sen, M.、Sinclair, B.(2015)。No! Formal theory, causal inference, and big data are not contradictory trends in political science。PS: Political Science & Politics,48(1),71-74。  new window
14.Lewis, G. B.(2017)。Do political science majors succeed in the labor market?。PS: Political Science & Politics,50(2),467-472。  new window
15.Lee, H.、Lee, J.-W.、Song, E.(2016)。Effects of educational mistmatch on wages in the Korean labor market。Asian Economic Journal,30(4),375-400。  new window
16.Iriondo, I.、Perez-Amaral, T.(2016)。The effect of educational mismatch on wages in Europe。Journal of Policy Modeling,38(2),304-323。  new window
17.Japec, L.、Kreuter, F.、Berg, M.、Biemer, P.、Decker, P.、Lampe, C.、Usher, A.(2015)。Big data in survey research: AAPOR task force report。Public Opinion Quarterly,79(4),839-880。  new window
18.Hanson, B.(2008)。Wither qualitative/quantitative? Grounds for methodological convergence。Quality & Quantity,42(1),97-111。  new window
19.Graham, E. R.、Shipan, C. R.、Volden, C.(2014)。The communication of ideas across subfields in political science。PS: Political Science & Politics,47(2),468-476。  new window
20.Gelman, Andrew、Basbøll, Thomas(2014)。When do stories work? Evidence and illustration in the social sciences。Sociological Methods & Research,43(4),547-570。  new window
21.Dryzek, J. S.(2006)。Revolutions without enemies: Key transformations in political science。American Political Science Review,100(4),487-492。  new window
22.Wendt, Alexander、Duvall, Raymond(2008)。Sovereignty and the UFO。Political Theory,36(4),607-633。  new window
23.King, Gary、Pan, Jennifer、Roberts, Margaret E.(2013)。How Censorship in China Allows Government Criticism but Silences Collective Expression。American Political Science Review,107(2),326-343。  new window
圖書
1.Silver, Nate(2012)。The signal and the noise: Why so many predictions fail-but some don't。New York, NY:Penguin。  new window
2.Hay, Colin(2002)。Political analysis: A critical introduction。Palgrave Macmillan。  new window
3.Geertz, Clifford(1973)。The Interpretation of Culture: Selected Essays。New York:Basic Books。  new window
4.Madsbjerg, C.、Rasmussen, M. B.(2014)。The moment of clarity: Using the human sciences to solve your toughest business problems。Harvard Business Review Press。  new window
5.Tetlock, P. E.、Gardner, D.(2016)。Superforecasting: The art and science of prediction。New York, NY:Broadway Books。  new window
6.Marsh, D.、Stoker, G.(2002)。Theory & methods in political science。Basingstoke:Palgrav Macmillan。  new window
7.Madsbjerg, Christian(2017)。Sensemaking: The power of the humanities in the age of the algorithm。New York, NY:Hachette Books。  new window
8.Lindstrom, Martin(2016)。Small data: The tiny clues that uncover huge trends。New York, NY:St. Martin's Press。  new window
9.Easton, David(1965)。A Framework for Political Analysis。Prentice-Hall。  new window
10.Lasswell, Harold Dwight(1936)。Politics: Who Gets What, When, How。New York, NY:Whittlesey House:McGraw-Hill。  new window
其他
1.Rasmussen, M. B.,Hansen, A. W.(2015)。Big data is only half the data marketers need,https://hbr.org/2015/11/big-data-is-only-half-the-data-marketers-need。  new window
2.de Neufville, R.(2012)。The political attack on political science,http://bigthink.com/politeia/the-political-attack-on-political-science。  new window
3.Uprichard, E.(2015)。Most big data is social data: The analytics need serious interrogation,http://blogs.lse.ac.uk/impactofsocialsciences/2015/02/12/philosophy-of-data-science-emma-uprichard/。  new window
4.Tencer, K.(2014)。Why big data only tells half the story,http://www.theglobeandmail.com/report-on-business/small-business/sb-managing/why-big-data-onlytells-half-the-story/article18541573/。  new window
 
 
 
 
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