Predicting faculty job centrality in communication

Abstract

Data from 1,581 faculty members affiliated with 98 doctoral-granting Communication programs in the United States were analyzed to determine normative publication rates and predictors of position centrality in the faculty hiring network. The Communication Institute for Online Scholarship (CIOS) database was used to measure publication frequency in refereed journals. Position centrality was measured using a Communication program's relative position in the hiring network as established by Barnett, Danowski, Feeley, and Stalker (2010). The average publication frequencies by academic rank were as follows: assistant professors averaged 2.29 articles (N = 441, SD = 3.29); associate professors averaged 6.69 articles (N = 497, SD = 5.77); professors averaged 10.92 articles (N = 542, SD = 12.09). Results from multiple regression analyses indicate the number of publications for faculty members and position centrality of where one earned his or her doctoral degree significantly predicted current position centrality. Publication numbers for one's advisor and year of earned doctorate did not emerge as significant predictors of position centrality.

The Evolution of Research in Journalism and Communication: An Analysis of Scholarly CIOS-Indexed Journals From 1915 to Present

“…The finding that 36% of the scholars in the CIOS database had published no articles confirms past work, roughly falling at the midpoint between Feeley et al (2011) 29% and Neuendorf et al (2007) (39%). This suggests that J&C units could redouble their outreach efforts to encourage further scholarship, as the plurality of scholars in J&C have published no articles, while the majority have published one or fewer articles.…”

“…As Feeley et al (2011) note, the predominance of speech communication programs and scholars on past productivity lists (e.g., Burroughs et al (1989); Griffin et al, 2018; Hickson et al, 2004) reflects content dimensions inherent to the original 24 journals included in the original Communication Index. By expanding that list over seven-fold to include a wider array of JMC outlets, the present study significantly extends past work, providing a more representative picture of the diversity of our field.…”

“…As the field of Journalism & Mass Communication (JMC) enters its second century, studies of programmatic and institutional quality are assuming greater importance in the current higher education landscape (e.g., Feeley et al, 2011). In the mass communication domain, such bibliometric work can help document larger trends in theory and methodology (e.g., Schweitzer, 1988).…”

“…While no single method is comprehensive, scholarly productivity rates are a valuable data point that can be used to communicate departmental contribution and prestige. Scholarly productivity research can help illuminate scholarly contributions made by individual faculty (e.g., Griffin et al, 2018) and full departments (Feeley et al, 2011). This information provides a vital data point that is necessary to consider when conducting holistic assessments about departmental value in the field of communication and at home institutions.…”

Identifying key papers within a journal via network centrality measures

“…A benefit of this centrality metric is that it can be used with valued or signed networks which use non-binary relationships between nodes (Bonacich 2007). Eigenvector centrality has been applied to social networks for purposes such as predicting academic positions for faculty members whose positions require publishing (Feeley et al 2010) as well as to examine the benefits gained within citation networks formed within different institutions on the basis that having knowledgeable coauthors provides a greater benefit to a paper (Liu et al 2015). Additionally, eigenvector centrality has been applied to numerous other fields: Allesina and Pascual (2009) apply eigenvector centrality to forecast the effects of species' extinctions; Joyce et al (2010) examine the ability of eigenvector centrality to identify critical nodes within brain networks; Kane (2009) applies eigenvector centrality to examine the influence of editors on the quality of articles within Wikipedia; and Lohmann et al (2010) demonstrate that this centrality metric is efficient for representing the brain's neural architecture.…”