Power-law distributions, the h-index, and Google Scholar (GS) citations: a test of their relationship with economics Nobelists

TL;DR

This study validates that Google Scholar citations for Nobel laureates in economics follow a power-law distribution aligned with their h-index and that their most cited works are on prize-winning topics, confirming GS's relevance for research evaluation.

Contribution

The paper demonstrates that Google Scholar can reliably reflect researchers' impact through power-law citation distributions and thematic relevance to their awards.

Findings

GS citations follow a power-law distribution with a clear h-index tail.

Top-cited works are on topics related to Nobel laureates' awarded research.

GS effectively captures research impact and thematic focus of Nobelists.

Abstract

This paper presents proof that Google Scholar (GS) can construct documentary sets relevant for evaluating researchers' works. Nobelists in economics were the researchers under analysis, and two types of tests of the GS cites to their works were performed: distributional and semantic. Distributional tests found that the GS cites to the laureates' works conformed to the power-law model with an asymptote or "tail" conterminous with their h-index demarcating their core oeuvre, validating both GS and the h-index. Semantic tests revealed that their works highest in GS cites were on topics for which they were awarded the prize.