Simulating growth of the h-index

An ARIMA-based study of bibliometric index prediction

Purpose

The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.

Design/methodology/approach

This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998–2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis.

Findings

ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well.

Research limitations/implications

This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research.

Originality/value

This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.

Does the hα-index reinforce the Matthew effect in science? The introduction of agent-based simulations into scientometrics

Recently, Hirsch (2019a) proposed a new variant of the h-index called the hα-index. The hα-index was criticized by Leydesdorff, Bornmann, and Opthof (2019) . One of their most important points is that the index reinforces the Matthew effect in science. The Matthew effect was defined by Merton (1968) as follows: “the Matthew effect consists in the accruing of greater increments of recognition for particular scientific contributions to scientists of considerable repute and the withholding of such recognition from scientists who have not yet made their mark” (p. 58). We follow up on the point about the Matthew effect in the current study by using a recently developed Stata command (h_index) and R package (hindex), which can be used to simulate h-index and hα-index applications in research evaluation. The user can investigate under which conditions hα reinforces the Matthew effect. The results of our study confirm what Leydesdorff et al. (2019) expected: The hα-index reinforces the Matthew effect. This effect can be intensified if strategic behavior of the publishing scientists and cumulative advantage effects are additionally considered in the simulation.

Academic inertia: Examining changes of scholarly output over time among academic minimally invasive surgeons

INTRODUCTION

The purpose of this study is to assess how the Hirsch Index (h-index) and other academic metrics change over time for academic minimally invasive surgeons (MIS).

METHODS

Through the Fellowship Council's website, MIS program-directors and associate program-directors were identified in 2017 and again in 2018. Using the Scopus database, the number of publications, citations, self-citations, and h-indices were calculated.

RESULTS

A total of 222 surgeons were included. The median increase of publications, citations, and h-index were 4, 134, and 1, respectively. 75% of surgeons (166/222) saw their h-index increase. In 2017, 26% of surgeons (57/222) had an increase of their h-index due to self-citation. One-year later, 35% of those surgeons (20/57) no longer demonstrated that change.

CONCLUSION

Self-citation remains infrequent within MIS. The h-index of most surgeons will increase over one-year. Many surgeons demonstrating an increase in h-index due to self-citation will see that change eliminated over time.

Statistical regularities in the rank-citation profile of scientists

Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile c(i)(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each c(i)(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different c(i)(r) profiles, our results demonstrate the utility of the β(i) scaling parameter in conjunction with h(i) for quantifying individual publication impact. We show that the total number of citations C(i) tallied from a scientist's N(i) papers scales as [Formula: see text]. Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress.