Mapping Approaches to 'Citizen Science' and 'Community Science' and Everything In-between: The Evolution of New Epistemic Territory?

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Title: Mapping Approaches to 'Citizen Science' and 'Community Science' and Everything In-between: The Evolution of New Epistemic Territory?
Authors: Hacking, Nick1,2 hackingn@cardiff.ac.uk, Lewis, Jamie1,2, Evans, Robert1,2
Source: Minerva: A Review of Science, Learning & Policy. Dec2024, Vol. 62 Issue 4, p549-572. 24p.
Subject Terms: *Scientific knowledge, Citizen science, Technology, Sociology, Science
Abstract: Over the last decade or so, the rate of growth of academic publications involving discussion of 'citizen science' and 'community science', and similar variants, has risen exponentially. These fluid terms, with no fixed definition, cover a continuum of public participation within a range of scientific activities. It is, therefore, apposite and timely to examine the evolving typologies of citizen science and community science and to ask how particular disciplinary actors are shaping content and usage. Do certain approaches to citizen science and community science activity remain siloed within specific disciplines or do some approaches resonate more widely? In this study, we use mixed methods—bibliometric and textual analysis—to chart the changing academic interpretations of this scientific activity over time. We then ask what these analyses mean for the future direction of academic research into citizen science and community science. The results suggest that, while certain disciplinary-based interpretations have been particularly influential in the past, a more epistemically mixed array of academic interests than was previously evident are currently determining expectations of what citizen science and community science should look like and what they can be expected to deliver. [ABSTRACT FROM AUTHOR]
Copyright of Minerva: A Review of Science, Learning & Policy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Over the last decade or so, the rate of growth of academic publications involving discussion of 'citizen science' and 'community science', and similar variants, has risen exponentially. These fluid terms, with no fixed definition, cover a continuum of public participation within a range of scientific activities. It is, therefore, apposite and timely to examine the evolving typologies of citizen science and community science and to ask how particular disciplinary actors are shaping content and usage. Do certain approaches to citizen science and community science activity remain siloed within specific disciplines or do some approaches resonate more widely? In this study, we use mixed methods—bibliometric and textual analysis—to chart the changing academic interpretations of this scientific activity over time. We then ask what these analyses mean for the future direction of academic research into citizen science and community science. The results suggest that, while certain disciplinary-based interpretations have been particularly influential in the past, a more epistemically mixed array of academic interests than was previously evident are currently determining expectations of what citizen science and community science should look like and what they can be expected to deliver. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Minerva: A Review of Science, Learning & Policy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2024
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