Tracking High-Impact Researchers Through Highly Cited Tails
Hirushan Sajindra, Eoin Ó Colgáin
Abstract
It is well recognised that field weighted citations are approximately lognormally distributed. This skewness reflects competition for attention, whereby most publications are gradually forgotten and only a minority continue to be recalled years later. Exploiting this observation, we construct a researcher-level indicator based on authorship of top 10% cited papers in All Science Journal Code (ASJC) bins and apply it to publications affiliated with Irish institutions over a typical funding cycle. Restricting attention largely to original research papers, we find that researchers authoring strictly more than one ASJC top-decile paper in a given year exhibit persistent participation in the highly cited tail of the literature in subsequent years, thus the simple track record indicator possesses predictive value. In contrast, the median researcher in cohorts of Research Ireland and European Research Council awardees selected through qualitative peer review either authors no top-decile paper or less than the median researcher in our ranking. These results demonstrate that research excellence as judged by qualitative peer review and influence on the research literature identify substantially different cohorts of researchers. In the absence of evidence, funding bodies and universities may conflate these two. While research reputation may be enhanced through many channels, highly cited papers constitute the most widely observable manifestation of international scholarly influence. Our analysis indicates that literature influence may be improved through greater use of data in track record pre-screening prior to qualitative peer review.
Create a lesson
Related papers
Modelling the complex dynamics of online disinformation cascades
Guillermo Romero Moreno, Valentin Robu, Han La Poutré
A data-driven heuristic-ensemble method for behavioural inference in collective-risk dilemma experiments
Marc Sadurní, Martín F. Díaz, Josep Perelló et al.
Learning Social Influence with Unobserved Attention
William Thompson, Moyi Tian, Linh Huynh et al.
A Gauge-Invariant Clustering Coefficient for Complex-Weighted Bipartite Networks
Anjali Waghmare, Yu Tian, Renaud Lambiotte
Breaking Behavioral Uniformity in Traffic Flow: Analytical Derivation and Numerical Verification of Optimal Cooperative Buffering
Raphael Korbmacher, Antoine Tordeux
Energy-barrier characterization of secured basins in coupled oscillators with inertia
Jaiyong Lee, Daekyung Lee, Seung-Woo Son et al.