Chapter A Natural Language Processing approach to measure expertise in the Delphi-based scenarios (Record no. 3089232)

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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 979-12-215-0106-3.29
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9791221501063
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Standard number or code 10.36253/979-12-215-0106-3.29
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Transcribing agency oapen
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Language code of text/sound track or separate title eng
042 ## - AUTHENTICATION CODE
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072 #7 - SUBJECT CATEGORY CODE
Subject category code J
Source bicssc
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Calleo, Yuri
Relator code auth
9 (RLIN) 1625362
245 10 - TITLE STATEMENT
Title Chapter A Natural Language Processing approach to measure expertise in the Delphi-based scenarios
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Florence
Name of publisher, distributor, etc Firenze University Press, Genova University Press
Date of publication, distribution, etc 2023
300 ## - PHYSICAL DESCRIPTION
Extent 1 electronic resource (6 p.)
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Series statement Proceedings e report
Volume number/sequential designation 134
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
Standardized terminology for access restriction Unrestricted online access
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Summary, etc The Delphi-based scenarios (DBS) development implies the assumption of different choices, through the gathering of information and the assessment of alternative resolutions (Panpatte and Takale, 2019). During the last decades, the spread of environmental hazards has increased quickly, much to request different responses in order to develop a sustainable future for humanity planning the present (McMichael and Lindgren, 2011). Since the DBS is a creative process (Nowack et al., 2011), different figures are selected to make choices, including academics, stakeholders and citizens. However, one of the main challenges remains the measurement of expertise, in fact, during the process, the experts should be assessed based on their competences in order to avoid any conflict in the final results and, eventually, weigh their answers. In recent years, some contributions adopted the self-assessments for the experts' evaluation (Sossa et al., 2019), but many issues still remain (such as strong subjectivity and cognitive biases which produce over or underestimation). We develop a new method to estimate the expertise by using Natural Language Processing to acquire information, extracting the contributions of experts in each topic. First, starting from a draft list of selected experts, we identify the category of reference (e.g., academia, industry, local authority, citizens etc.). We build a data repository with the personal pages (URLs) of each expert to then use Python to extract from the URLs, the number of contributions related to a keyword, different for each category (e.g., publications for academics, reports and projects for stakeholders etc.). Finally, we proceed adopting a coefficient of production with a weighted sum of the results. To practically demonstrate our approach, we applied this method to a cohort of known experts, part of the "Smart control of the climate resilience" (SCORE) H2020 European project to estimate their expertise in specific areas.
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction Creative Commons
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Uniform Resource Identifier <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>
546 ## - LANGUAGE NOTE
Language note English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Society & social sciences
Source of heading or term bicssc
9 (RLIN) 102798
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Delphi
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Uncontrolled term expertise assessment
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Uncontrolled term future scenarios
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Di Zio, Simone
Relator code auth
9 (RLIN) 1624824
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Pilla, Francesco
Relator code auth
9 (RLIN) 1658021
773 1# - HOST ITEM ENTRY
Title ASA 2022 Data-Driven Decision Making
Control subfield nnaa
Other item identifier OAPEN Library UUID: 863aa499-dbee-4191-9a14-3b5d5ef9e635
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Uniform Resource Identifier <a href="https://library.oapen.org/bitstream/id/fb1b08e7-9a1e-4b8c-a613-231b80f6c7cf/9791221501063-29.pdf">https://library.oapen.org/bitstream/id/fb1b08e7-9a1e-4b8c-a613-231b80f6c7cf/9791221501063-29.pdf</a>
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Public note Open Access: OAPEN Library, download the publication
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://library.oapen.org/handle/20.500.12657/74901">https://library.oapen.org/handle/20.500.12657/74901</a>
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Public note Open Access: OAPEN Library: description of the publication
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