Collaboration networks and scientific quality among behavioural ecologists

Pike, Thomas (2010) Collaboration networks and scientific quality among behavioural ecologists. Behavioral Ecology, 21 (2). pp. 431-435. ISSN 1045-2249

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Full text URL: http://dx.doi.org/10.1093/beheco/arp194

Abstract

Quantifying an author’s scientific impact is becoming increasingly important for evaluation and comparison purposes (e.g., for university recruitment and advancement or the award of grants). To this end, a number of quantitative metrics have been proposed that (in principle) allow the comparison of individuals’ scientific quality or impact (Cartwright and McGhee 2005; Cheek et al. 2006; Meho 2007; although for a discussion on the potential pitfalls of this approach, see e.g., Garfield 1979; MacRoberts and MacRoberts 1996). These generally fall into the categories of reputation, yield or productivity, and influence or impact (Avital and Collopy 2001). Commonly used metrics include the total number of papers published, which is commonly used to gauge basal productivity and is likely to be positively correlated with factors such as funding obtained and research group size; the mean or total number of citations received, which are assumed to indicate the scientific utility of a study and can thus be used as a partial indicator of a study’s quality and impact (Lawani 1986); and the journals where the papers were published and these journals’ impact parameter (e.g., Steinpreis et al. 1999). In particular, publication productivity and measures of citation frequency are commonly used to assess influence, although other metrics have also been proposed that incorporate both measures into a single metric. For example, the h-index, developed by Hirsch (2005), aims to measure the cumulative impact of a researcher’s output by looking at the quantity of papers published along with the number citations his/her work has received. The effective evaluation of such metrics is likely to require an understanding of factors such as visibility, the size of citing community, and the extent of integration into social and professional networks (Ward et al. 1992). However, the relationship between these factors and scientific impact is unclear. Here, I investigate how variation in individual scientific impact is related to the structure of the scientific collaboration network (specifically, the coauthorship network) of which they are a part.

Item Type:Article
Additional Information:Quantifying an author’s scientific impact is becoming increasingly important for evaluation and comparison purposes (e.g., for university recruitment and advancement or the award of grants). To this end, a number of quantitative metrics have been proposed that (in principle) allow the comparison of individuals’ scientific quality or impact (Cartwright and McGhee 2005; Cheek et al. 2006; Meho 2007; although for a discussion on the potential pitfalls of this approach, see e.g., Garfield 1979; MacRoberts and MacRoberts 1996). These generally fall into the categories of reputation, yield or productivity, and influence or impact (Avital and Collopy 2001). Commonly used metrics include the total number of papers published, which is commonly used to gauge basal productivity and is likely to be positively correlated with factors such as funding obtained and research group size; the mean or total number of citations received, which are assumed to indicate the scientific utility of a study and can thus be used as a partial indicator of a study’s quality and impact (Lawani 1986); and the journals where the papers were published and these journals’ impact parameter (e.g., Steinpreis et al. 1999). In particular, publication productivity and measures of citation frequency are commonly used to assess influence, although other metrics have also been proposed that incorporate both measures into a single metric. For example, the h-index, developed by Hirsch (2005), aims to measure the cumulative impact of a researcher’s output by looking at the quantity of papers published along with the number citations his/her work has received. The effective evaluation of such metrics is likely to require an understanding of factors such as visibility, the size of citing community, and the extent of integration into social and professional networks (Ward et al. 1992). However, the relationship between these factors and scientific impact is unclear. Here, I investigate how variation in individual scientific impact is related to the structure of the scientific collaboration network (specifically, the coauthorship network) of which they are a part.
Keywords:Coauthorship, h-index, Social networks
Subjects:C Biological Sciences > C120 Behavioural Biology
Divisions:College of Science > School of Life Sciences
ID Code:4414
Deposited By: Tom Pike
Deposited On:13 Apr 2011 15:52
Last Modified:13 Mar 2013 08:59

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