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<dc:title>Let’s go fishing: A quantitative analysis of subsistence choices with a special focus on mixed economies among small-scale societies</dc:title>
<dc:creator>Ahedo García, Virginia</dc:creator>
<dc:creator>Zurro, Débora</dc:creator>
<dc:creator>Caro Saiz, Jorge</dc:creator>
<dc:creator>Galán Ordax, José Manuel</dc:creator>
<dcterms:abstract>The transition to agriculture is regarded as a major turning point in human history. In the present contribution we propose to look at it through the lens of ethnographic data by means of a machine learning approach. More specifically, we analyse both the subsistence economies and the socioecological context of 1290 societies documented in the Ethnographic Atlas with a threefold purpose: (i) to better understand the variability and success of human economic choices; (ii) to assess the role of environmental settings in the configuration of the different subsistence economies; and (iii) to examine the relevance of fishing in the development of viable alternatives to cultivation. All data were extracted from the publicly available cross-cultural database D-PLACE. Our results suggest that not all subsistence combinations are viable, existing just a subset of successful economic choices that appear recurrently in specific ecological systems. The subsistence economies identified are classified as either primary or mixed economies in accordance with an information-entropy-based quantitative criterion that determines their degree of diversification. Remarkably, according to our results, mixed economies are not a marginal choice, as they constitute 25% of the cases in our data sample. In addition, fishing seems to be a key element in the configuration of mixed economies, as it is present across all of them.</dcterms:abstract>
<dcterms:dateAccepted>2022-01-18T08:03:53Z</dcterms:dateAccepted>
<dcterms:available>2022-01-18T08:03:53Z</dcterms:available>
<dcterms:created>2022-01-18T08:03:53Z</dcterms:created>
<dcterms:issued>2021-08</dcterms:issued>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:identifier>1932-6203</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/6334</dc:identifier>
<dc:identifier>10.1371/journal.pone.0254539</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>PLoS ONE. 2021, V. 16, n. 8, e0254539</dc:relation>
<dc:relation>https://doi.org/10.1371/journal.pone.0254539</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/HAR2017-90883-REDC/ES/Simular el pasado para entender el comportamiento humano</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102518-T/ES/SISTEMAS COMPLEJOS SOCIOTECNOLOGICOS</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/HAR2016-77672-P/ES/MODELADO DEL CULTIVO EN LA PREHISTORIA</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/AGAUR//2017 SGR 212/</dc:relation>
<dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>Atribución 4.0 Internacional</dc:rights>
<dc:publisher>Public Library Science</dc:publisher>
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