What is an expert? A systems perspective on expertise

被引:37
|
作者
Caley, Michael Julian [1 ]
O'Leary, Rebecca A. [2 ]
Fisher, Rebecca [2 ]
Low-Choy, Samantha [3 ]
Johnson, Sandra [3 ]
Mengersen, Kerrie [3 ]
机构
[1] Australian Inst Marine Sci, Townsville, Qld 4810, Australia
[2] UWA Oceans Inst, Australian Inst Marine Sci, Crawley, WA 6009, Australia
[3] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld 4001, Australia
来源
ECOLOGY AND EVOLUTION | 2014年 / 4卷 / 03期
关键词
Bayesian network; expert judgement; expert knowledge; expert opinion; hierarchy of classes; supra-expert; taxonomy; BAYESIAN NETWORK; SPECIES RICHNESS; ELICITATION; KNOWLEDGE; OPINION; UNCERTAINTY; DISCOVERY; FRAMEWORK; IMPACT;
D O I
10.1002/ece3.926
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Expert 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.
引用
收藏
页码:231 / 242
页数:12
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