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dc.contributor.authorCaley, Michael Julian
dc.contributor.authorO'Leary, Rebecca A
dc.contributor.authorFisher, Rebecca
dc.contributor.authorLow-Choy, Samantha
dc.contributor.authorJohnson, Sandra
dc.contributor.authorMengersen, Kerrie
dc.date.accessioned2018-01-11T00:45:19Z
dc.date.available2018-01-11T00:45:19Z
dc.date.issued2014
dc.identifier.issn2045-7758
dc.identifier.doi10.1002/ece3.926
dc.identifier.urihttp://hdl.handle.net/10072/173213
dc.description.abstractExpert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) was chosen for this purpose. For illustration, we focused on taxonomic expertise. The model structure was developed in consultation with taxonomists. The relative importance of the factors within the network was determined by a second set of taxonomists (supra-experts) who also provided validation of the model structure. Model performance was assessed by applying the model to hypothetical career states of taxonomists designed to incorporate known differences in career states for model testing. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher-order nodes, “Quality of work” and “Total productivity”, had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite the differences in the node weights provided by our supra-experts, there was good agreement among assessments of our hypothetical experts that accurately reflected differences we had specified. This systems approach provides a way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherJohn Wiley & Sons
dc.relation.ispartofpagefrom231
dc.relation.ispartofpageto242
dc.relation.ispartofissue3
dc.relation.ispartofjournalEcology and Evolution
dc.relation.ispartofvolume4
dc.subject.fieldofresearchEcology not elsewhere classified
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchEvolutionary Biology
dc.subject.fieldofresearchcode060299
dc.subject.fieldofresearchcode0602
dc.subject.fieldofresearchcode0603
dc.titleWhat is an expert? A systems perspective on expertise
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttps://creativecommons.org/licenses/by/3.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorLow-Choy, Sama J.


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