مرور افقی در پایگاه داده تحقیقی
ترجمه نشده

مرور افقی در پایگاه داده تحقیقی

عنوان فارسی مقاله: مرور افقی در پایگاه داده تحقیقی سیاست گذاری با مدل مبحث احتمالی
عنوان انگلیسی مقاله: Horizon scanning in policy research database with a probabilistic topic model
مجله/کنفرانس: پیش بینی فناورانه و تغییرات اجتماعی – Technological Forecasting and Social Change
رشته های تحصیلی مرتبط: مدیریت
کلمات کلیدی فارسی: پیش بینی، مرور افقی، پایگاه داده تحقیقی سیاست گذاری، مدلسازی مبحث، تخصیص Dirichlet نهفته
کلمات کلیدی انگلیسی: Foresight، Horizon scanning، Policy research database، Topic modeling، Latent Dirichlet allocation
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.techfore.2018.02.007
دانشگاه: Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
صفحات مقاله انگلیسی: 7
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.852 در سال 2018
شاخص H_index: 93 در سال 2019
شاخص SJR: 1.422 در سال 2019
شناسه ISSN: 0040-1625
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13387
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Background

3. Research methodology

4. Topic structure in policy research database

5. Discussion and conclusion

Acknowledgment

References

بخشی از مقاله (انگلیسی)

Abstract

National governments take advantage of collective intelligence when conducting foresight processes. They grasp emerging issues through expert reviews as well as public opinions. It raises national agendas and affects policymaking process. Therefore, by examining policy papers which contain societal issues, we can perceive past, current, and future environments. In this study, we exploit policy research database of Republic of Korea, which is a unique source that automatically collects all policy papers written by national research institutes, to extract latent topics and their trends over 10 years through a probabilistic topic model. Detected topics fairly correspond to expert-selected future drivers in national foresight report, implying that public discourse and policy agenda are coupled. We suggest to utilize open government data and text mining methods for building open foresight framework that various actors exchange their opinions on societal issues.

Introduction

The history of capitalism after World War II can be understood as a reconstruction process of economic system. Foresight led countries to build effective innovation system, focusing on the role of science and technology. Since 1970s when market matured, systemic view has been underlined in designing foresight process (Andersen and Andersen, 2014). It is hard to understand a dynamic environment without considering collective behaviors of actors. Hence, national governments include not only expert reviews but also public opinions to reflect social complexity in foresight. Collective intelligence helps governments to detect weak signals and to shape emerging issues. For successful national foresight, collaboration between departments, external knowledge sources, and credibility of evidences are also needed (Habegger, 2010). To meet these requirements, we pay attention to open government which enhances transparency and collaborative capacity of national government (Lathrop and Ruma, 2010). Republic of Korea is taken as a subject of study because of its numerous foresight experiences and high open government availability (OECD, 2017). Foresight of Korea has two characteristics. First, it is coherent with STI public policy (Ahn, 2017), leading a success in catch-up development. Preliminary feasibility study is conducted by the government before launching a new national project.