000 | 04361nam a22006015i 4500 | ||
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001 | 978-3-031-16624-2 | ||
003 | DE-He213 | ||
005 | 20240423125321.0 | ||
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008 | 230123s2023 sz | s |||| 0|eng d | ||
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_a9783031166242 _9978-3-031-16624-2 |
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_a10.1007/978-3-031-16624-2 _2doi |
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_aHandbook of Computational Social Science for Policy _h[electronic resource] / _cedited by Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
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300 |
_aXXI, 490 p. 1 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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506 | 0 | _aOpen Access | |
520 | _aThis 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 problemsin 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 datathat can be used to study social sciences and are interested in achieving a policy impact. | ||
650 | 0 |
_aArtificial intelligence _xData processing. |
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650 | 0 | _aQuantitative research. | |
650 | 0 |
_aSociology _xMethodology. |
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650 | 0 | _aMachine learning. | |
650 | 1 | 4 | _aData Science. |
650 | 2 | 4 | _aData Analysis and Big Data. |
650 | 2 | 4 | _aSociological Methods. |
650 | 2 | 4 | _aMachine Learning. |
700 | 1 |
_aBertoni, Eleonora. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aFontana, Matteo. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aGabrielli, Lorenzo. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aSignorelli, Serena. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aVespe, Michele. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031166235 |
776 | 0 | 8 |
_iPrinted edition: _z9783031166259 |
776 | 0 | 8 |
_iPrinted edition: _z9783031166266 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-16624-2 |
912 | _aZDB-2-SCS | ||
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