東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Handbook of computational social sci...

Bertoni, Eleonora.

FindBook

Google Book

Amazon

博客來

Handbook of computational social science for policy

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Handbook of computational social science for policy/ edited by Eleonora Bertoni ... [et al.].
其他作者:	Bertoni, Eleonora.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xxi, 490 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Social sciences - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-16624-2
ISBN:	9783031166242

Handbook of computational social science for policy
Handbook of computational social science for policy[electronic resource] /edited by Eleonora Bertoni ... [et al.]. - Cham :Springer International Publishing :2023. - xxi, 490 p. :ill., digital ;24 cm.

Open access.

This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding data that can be used to study social sciences and are interested in achieving a policy impact.

ISBN: 9783031166242

Standard No.: 10.1007/978-3-031-16624-2doiSubjects--Topical Terms:

529012
Social sciences
--Data processing.

LC Class. No.: H61.3

Dewey Class. No.: 300.727

Handbook of computational social science for policy
LDR:03054nmm a2200325 a 4500 001 2315574
003 DE-He213
005 20230124114014.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031166242 $q (electronic bk.)
020 $a 9783031166235 $q (paper)
024 7 $a 10.1007/978-3-031-16624-2 $2 doi
035 $a 978-3-031-16624-2
040 $a GP $c GP
041 0 $a eng
050 4 $a H61.3
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 300.727 $2 23
090 $a H61.3 $b .H236 2023
245 0 0 $a Handbook of computational social science for policy $h [electronic resource] / $c edited by Eleonora Bertoni ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xxi, 490 p. : $b ill., digital ; $c 24 cm.
506 $a Open access.
520 $a This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding data that can be used to study social sciences and are interested in achieving a policy impact.
650 0 $a Social sciences $x Data processing. $3 529012
650 0 $a Social sciences $x Methodology. $3 525593
650 1 4 $a Data Science. $3 3538937
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Sociological Methods. $3 3594063
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Bertoni, Eleonora. $3 3628022
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-16624-2
950 $a Computer Science (SpringerNature-11645)