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Developing a hetero-intelligence methodological framework for sustainable policy-making based on the assessment of large language models

2024-06Journal article Open access
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dc.contributor.authorBuitrago Esquinas, Eva María
dc.contributor.authorPuig-Cabrera, Miguel
dc.contributor.authorSantos, José António C.
dc.contributor.authorCustódio Santos, Margarida
dc.contributor.authorYñiguez, Rocío
dc.date.accessioned2025-01-17T10:38:57Z
dc.date.available2025-01-17T10:38:57Z
dc.date.issued2024-06
dc.description.abstractThis work delves into the increasing relevance of Large Language Models (LLMs) in the realm of sustainable policy-making, proposing an innovative hetero-intelligence framework that blends human and artificial intelligence (AI) for tackling modern sustainability challenges. The research methodology includes a hetero-intelligence performance test, which juxtaposes human intelligence with AI in the formulation and implementation of sustainable policies. After testing this hetero-intelligence methodology, seven steps are rigorously described so that it can be replicated in any sustainability planning related context. The results underscore the capabilities and limitations of LLMs, underscoring the critical role of human intelligence in enhancing the efficacy of hetero-intelligence systems. This work fulfils the need of a rigorous methodological framework based on empirical steps that can provide unbiased outcomes to be integrated into sustainable planning and decision-making processes.eng
dc.identifier.doi10.1016/j.mex.2024.102707
dc.identifier.issn2215-0161
dc.identifier.urihttp://hdl.handle.net/10400.1/26642
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationResearch Centre for Tourism, Sustainability and Well-being
dc.relation.ispartofMethodsX
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSustainable planning and policy
dc.subjectHetero-intelligent performance testing
dc.subjectConversational generative AI
dc.subjectHuman intelligence
dc.subjectLarge language models
dc.subjectChatGPT
dc.titleDeveloping a hetero-intelligence methodological framework for sustainable policy-making based on the assessment of large language modelseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre for Tourism, Sustainability and Well-being
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04020%2F2020/PT
oaire.citation.startPage102707
oaire.citation.titleMethodsX
oaire.citation.volume12
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBuitrago Esquinas
person.familyNamePuig-Cabrera
person.familyNameSantos
person.familyNameCustódio Santos
person.familyNameYñiguez
person.givenNameEva María
person.givenNameMiguel
person.givenNameJosé António C.
person.givenNameMargarida
person.givenNameRocío
person.identifier.ciencia-idBE16-A0CC-8341
person.identifier.ciencia-id4816-E98C-E353
person.identifier.ciencia-idA71F-B4FC-F35C
person.identifier.ciencia-idB919-91DD-CFD9
person.identifier.orcid0000-0003-4113-5836
person.identifier.orcid0000-0003-4524-9830
person.identifier.orcid0000-0002-2675-3487
person.identifier.orcid0000-0003-3383-5699
person.identifier.orcid0000-0002-7370-6632
person.identifier.ridK-4997-2014
person.identifier.ridU-9656-2019
person.identifier.ridE-6838-2010
person.identifier.scopus-author-id55293654400
person.identifier.scopus-author-id57198436773
person.identifier.scopus-author-id57192278257
person.identifier.scopus-author-id36103636600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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