Bibliometric indicators: the origin of their log-normal distribution and why they are not a reliable proxy for an individual scholar’s talent

There is now compelling evidence that the statistical distributions of extensive individual bibliometric indicators collected by a scholar, such as the number of publications or the total number of citations, are well represented by a Log-Normal function when homogeneous samples are considered. A Log-Normal distribution function is the normal distribution for the logarithm of the variable. In linear scale it is a highly skewed distribution with a long tail in the high productivity side. We are still lacking a detailed and convincing ab-initio model able to explain observed Log-Normal distributions-this is the gap this paper sets out to fill. Here, we propose a general explanation of the observed evidence by developing a straightforward model based on the following simple assumptions: (1) the materialist principle of the natural equality of human intelligence, (2) the success breeds success effect, also known as Merton effect, which can be traced back to the Gospel parables about the Talents (Matthew) and Minas (Luke), and, (3) the recognition and reputation mechanism. Building on these assumptions we propose a distribution function that, although mathematically not identical to a Log-Normal distribution, shares with it all its main features. Our model well reproduces the empirical distributions, so the hypotheses at the basis of the model are not falsified. Therefore the distributions of the bibliometric parameters observed might be the result of chance and noise (chaos) related to multiplicative phenomena connected to a publish or perish inflationary mechanism, led by scholars' recognition and reputations. In short, being a scholar in the right tail or in the left tail of the distribution could have very little connection to her/his merit and achievements. This interpretation might cast some doubts on the use of the number of papers and/or citations as a measure of scientific achievements. A tricky issue seems to emerge, that is: what then do bibliometric indicators really measure? This issue calls for deeper investigations into the meaning of bibliometric indicators. This is an interesting and intriguing topic for further research to be carried out within a wider interdisciplinary investigation of the science of science, which may include elements and investigation tools from philosophy, psychology and sociology

A single journal study : Malaysian Journal of Computer Science

Single journal studies are reviewed and measures used in the studies are highlighted. The following quantitative measures are used to study 272 articles published in Malaysian Journal of Computer Science, (1) the article productivity of the journal from 1985 to 2007, (2) the observed and expected authorship productivity tested using Lotka's Law of author productivity, identification and listing of core authors; (3) the authorship, co-authorship pattern by authors' country of origin and institutional affiliations; (4) the subject areas of research; (5) the citation analysis of resources referenced as well as the age and half-life of citations; the journals referenced and tested for zonal distribution using Bradford's law of journal scattering; the extent of web citations; and (6) the citations received by articles published in MJCS and impact factor of the journal based on information obtained from Google Scholar, the level of author and journal self-citation

A Review of Theory and Practice in Scientometrics

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the “laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments