Eynde, Ana and Laspra, Belén and García, Irene (2017) Exploring the Image of Science: Neural Nets and the PIKA Model. Advances in Research, 9 (5). pp. 1-19. ISSN 23480394
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Abstract
In spite of the efforts devoted to understand the relationship of society with science, the results have not been satisfactory. The aim of this study is to test the PIKA model, developed to contribute to a better understanding of the perspective of citizens in the relationship of society with science. Our hypothesis is that the interaction of citizens with science generates an image that determines how they react to it. We conceive this image as a mental map, and according to contributions from neurology, we consider that it is grounded on a neural net. The PIKA model postulates that there is a section of the image of science that accounts for the interaction of Perception, Interest, Knowledge, and willingness to Act. We used Structural Equation Modelling to obtain evidence to support this model. We used data from three Spanish samples: the 2006 and 2014 editions of the Survey on Social Perception of Science and Technology by the Spanish Foundation for Science and Technology, and the answers to the PIKA Questionnaire of a sample of students from several Spanish universities. The sample of the 2006 edition of the survey of FECYT is comprised by 7.056 subjects from 18 years of age, while the 2014 edition includes 6.136 people. The sample that has completed the PIKA questionnaire includes 2.138 students from some Spanish universities. The results provide evidence in favour of the PIKA model in the three samples. We conclude that the image of science depicted as a neural net is useful to explain the interaction of citizens with science. Nevertheless, to achieve better understanding of this interaction we need better indicators of the factors that give shape to the citizens' image of science.
Item Type: | Article |
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Subjects: | STM Open Press > Multidisciplinary |
Depositing User: | Unnamed user with email support@stmopenpress.com |
Date Deposited: | 05 May 2023 10:49 |
Last Modified: | 06 Jul 2024 07:03 |
URI: | http://journal.submissionpages.com/id/eprint/1161 |