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Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability

Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability

Chris William Callaghan
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 15
ISSN: 1941-6253|EISSN: 1941-6261|EISBN13: 9781522565017|DOI: 10.4018/IJSKD.2019040101
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MLA

Callaghan, Chris William. "Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability." IJSKD vol.11, no.2 2019: pp.1-15. http://doi.org/10.4018/IJSKD.2019040101

APA

Callaghan, C. W. (2019). Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability. International Journal of Sociotechnology and Knowledge Development (IJSKD), 11(2), 1-15. http://doi.org/10.4018/IJSKD.2019040101

Chicago

Callaghan, Chris William. "Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability," International Journal of Sociotechnology and Knowledge Development (IJSKD) 11, no.2: 1-15. http://doi.org/10.4018/IJSKD.2019040101

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Abstract

Microbes learn through exponential processes, whereby they reproduce in exponential form (one can produce two, which can produce four, and so on). A single mutation against an antibiotic, for example, can form the basis for a new strain, via this process of exponential replication, which is conceptualised here as microbial ‘learning.' This article plots the hypothesised trajectory of microbial learning against that of human research and development (R&D) efforts, or R&D learning that is tasked with replacing the categories of antibiotic drugs that are failing. The research problem addressed in this research is the failure of scientific research, conceptualised as human learning, to keep pace with problems such as the growth in antibiotic resistance, or microbial learning. Open processes of learning described in terms of networked science theory are identified as an important theoretical framework within which to locate this knowledge problem. In light of potentially catastrophic threats like total antibiotic failure, it is argued that the formalisation of a scientific methodology in the form of crowdsourced R&D derived from networked science principles may offer useful insights into how to improve R&D efficiency and effectiveness, across fields and contexts.

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