Study of Lotka’s Law and Authorship Pattern in the Conference Proceedings of COLLNET: an International Conference Covering Exclusively Bibliometric Study

Maya Verma¹,*, Pratima Rajiv²

¹School of Studies in Library and Information Science, Pt. Ravishankar Shukla University, Raipur, C. G. (India)

²Govt. Kavyopadhyay Hiralal College, Abhanpur, Raipur, C. G. (India)

*Corresponding Author:  verma_maya64@rediffmail.com

Keywords: Lotka’s Law; Authorship Pattern; Conference Proceedings; Bibliometrics.

Introduction:

Bibliometrics is a type of research method used by researchers in different subject disciplines. “Bibliometric techniques are used for a variety of purposes like the determination of various scientific indicators, the evaluation of scientific output, the selection of journals for libraries and even for forecasting of potential Nobel laureates. Bibliometric analysis has become a well-established part of information research. The most obvious use of Bibliometric data is to improve bibliographic control, as it is not possible to provide efficient secondary services without knowing the size and characteristics of literature. Bibliometrics has grown out of the realization that literature is growing and changing at a rate in which no librarian or information worker equipped with traditional bibliographic methods and skills could keep abreast. (Zafrunnisha 10).

The two important elements of bibliometric studies are:

I.          Authors Productivity

II.       Citation Analysis

Author’s Productivity:

The author’s productivity study is conducted to identify the noble laureates of any subject area. Bibliometric tools are used for this purpose. Lotka’s law of scientific productivity which is a bibliometric law is applied to the data of the author’s productivity. This helps to identify how many authors are productive in research publications. This also helps to identify the key contributors from organizations, states or countries in which data can be useful in policy formulations, grants distributions, etc.

Citation Analysis:

Citation analysis is mainly conducted to measure the contribution of a specific work of research to the growth of any discipline of study. It is used to identify the good papers, standard authors, or journals in a specific field. Bibliometric laws like Bradford’s law can be applied to the ranked journals to validate the study.

Literature Review:

Maz Machado, Alexander et al.(2017)  presents a bibliometric study on the fit of Lotka’s law on Information Science & Library Science journals indexed in Social Science Citation Index of Journal Citation Report from the period 1956 to 2014. The parameters of the Lotka's law model, C and α, were found using the linear least squares method and the Kolmogorov-Smirnov test was applied to estimate the kindness of adjustment of the results to the Lotka’s distribution. It was found that the pattern of publication of the LIS category articles fits to Lotka’s law.

Suresh Kumar, P.K. (2017) examines authorship pattern of 556 papers published in Journal of Documentation during 2003 to 2015. In addition to the papers, a sample of 1550 references from a population of 15,529 unique references given at the end of the papers was selected using simple random sample method. It was found that almost half of the publications were written by single authors. Lotka’s Law was tested on the resulting 2106 publications using Kolmogorov-Smrinov goodness-of-fit. The K-S test and the author productivity graph revealed that Lotka’s law was applicable to the set LIS publications.

Objective of the study:

1.      To study the authorship pattern in the proceedings.

2.      To examine the validity of Lotka’s law.

Hypothesis:

1.      The authorship distribution in the conference proceedings under the studied period fits to the Lotka’s law.

2.      The single authored papers are lesser then multi authored papers.

Methodology:

The proceedings are collected from different authors who have contributed their papers in the conferences during the period of study. The period of study is from 2011-2015(five years).In the current study all the authors are given equal credits. Ranking of authors is done using MS Excel. For validating Lotka’s law, the Kolmogorov-Smirnov goodness-of-fit test is used to compare the function describing the observed and theoretical distribution of publication at 0.10 level of significance.

Authors Productivity:

Counting authors productivity is an important aspect of bibliometric study. Various aspects of contributing authors are considered for analysis like figure out the top contributing authors in a particular area. The studies like top contributing authors (authors in any position and only the first author) are also calculated separately. Authorship pattern is also studied.

Top Contributing authors:

The objective of bibliometric study is vast where various calculations are done to reach to a concrete result. One of the objectives of this study is to identify the most prolific author’s from the data under study. This identification is hoped to be useful for the professionals who looks for expert in a specific subject field, as an expert can only contribute a significant number of articles in an area.

The authors are given equal credits in spite of their position in writing the paper. The table is prepared based on the number of papers they have contributed to the conference proceedings during the period of study.

Table: 1 Name of Ranked Authors (COLLNET) (Author in any position)

The above table gives the name of the most prolific authors of COLLNET conference. Dr. Haiyan Hou, Professor, Dalian University of Technology, is the author who has contributed maximum articles to COLLNET Conference proceedings with nine papers in a span of five year. Second rank is held by two authors, Dr R K Verma, Scientist, NISCAIR and Professor Dr Sudhir Kumar contributed eight papers each and held second rank in the list. Third rank is held by two authors, Grant Lewison, Senior Research Fellow, Kings College, and Hildrun Kretschmer, visiting professor with seven papers each. Here it can be said that these people are experts in the field of Biliometric study.

Ranking of authors:

A ranked list is prepared based on the number of papers they have contributed during the period of study.

Table 2: Ranking of Authors (COLLNET) (Author in any position)

In the above table, it can be seen that among 685 authors 0.15% author have contributed maximum papers which is nine, 0.29% author have contributed eight papers, 0.29% have contributed seven papers each, 0.58% authors have contributed six papers each, 1.46% have contributed five papers, 2.63% of authors have contributed four papers. 6.13% authors have contributed three articles and 14.89% authors have contributed two articles each. 73.58% authors have a single paper contribution in this specific conference proceeding in the span of five years.

Authorship Pattern:

Authorship pattern is studied to understand the collaboration behavior of the authors as it has a great impact on research. The authorship pattern is shifting from solo work to collaborative works now a day. In this study too it is tried to understand that whether there is any change in the trend or not.

Table 3: Authorship Pattern

From the above table it can be seen that two authored papers are higher with 40.6% of the total papers, where as single authored papers. The percentage of single authored papers and three authored papers are almost same. It is not commonly seen that more then four authors write research or it can be said that it is not a common practice. But here it can be seen that 11.5% papers are written by four authors in collaboration. So here the hypothesis is proved that ‘The single authored papers are lesser then multi authored papers.

Degree of Collaboration:

The degree of collaboration is defined as the ratio of the number of collaborative research papers to the total number of research papers in the discipline during a certain period of time. The formula suggested by Subramanyam is used in this study. It is expressed as where;

C = N (m)/ (N (m) + N (s))  

C is the degree of collaboration in a discipline.

Nm is the number of multi-authored research papers in the discipline published during a year.

Ns is the number of single authored research papers in the discipline published during a year.

Table: 4 Degree of Collaboration

Here it can be seen that the degree of collaboration is very high.

Author’s productivity and Application of Lotka’s law:

Alfred James Lotka in his study emphasise that It stated that “... the number of authors making n contributions is about 1/of those making one; and the proportion of all contributors, that make a single contribution, is about 60 percent”. . Lotka’s Law is often called “inverse square law” indicating that there is an inverse relation between the number of publications and the number of authors producing these publications. The generalized form of Lotka’s Law can be expressed as “”, where y is the number of authors with x articles, the exponent n and constant c are parameters to be estimated from a given set of author productivity data.

This paper used the least square method given by Pao. The n value is calculated by this method using the following formula:

Here N= Number of pairs of data

X= Logarithm of articles(x) and Y=Logarithm of authors (y)

The value of constant C is calculated using the following formula.

 Results of the Study:

The first step in the testing of Lotka’s law is to determine the value of n.

Table: 5 Determination of exponent value of ‘n’

Table: 6 Computation of ‘c’

Suitability of Lotka’s law using Kolmogorov-Smirnov (K-S) Test:

Table: 5.2.5.2.3 Observed & Expected Distribution of Authors (COLLNET)

 Kolmogorov-Smirnov (K-S) goodness of fit Statistics was found at 0.10 level of significance. It was found that Dmax > Critical value. Thus, null hypothesis is rejected and it is concluded that the Observed distribution is different from theoretical distribution predicted by Lotka's law. The law does not fit in the observed distribution of COLLNET.

 Conclusion:

The paper examines the authorship pattern, Degree of Collaboration and testing of Lotka’s law to the present dataset. The data is taken from the conference Proceedings of COLLNET Conference which is an international conference dedicated to Bibliometric, Scientometric and Webometric study. The authorship pattern in the conference proceeding is studied and it is found that more authors are preferring to write in collaboration, than writing independently. Lotka’s law of author productivity is considered as a classical law of bibliometrics. In this study it is found that Lotka’s law is not applicable to the current data set.

References:

Maz Machado, Alexander et al. “Empirical Examination of Lotka’s Law for Information Science and Library Science.” Pakistan Journal of Information Management & Libraries (PJIM&L), 19(2017): 37-51. Web.16th April 2018. Online:  http://eprints.rclis.org/32817/1/Lotka’s Law.pdf

Navaneethakrishnan, Subramanian, "Authorship patterns and degree of collaboration of Sri Lankan scientific publications in Social sciences and Humanities – a picture from SCOPUS".Library Philosophy and Practice. 1153(2014). Web, 12th August 2018. Online: http://digitalcommons.unl.edu/libphilprac/1153

Osarih,farideh, Esmaeel, Mostafavi(2011) ‘Lotka’s Law and authorship distribution in Computer Science using Web of Science (WoS) during 1986–2009’ COLLNET Journal of Scientometric and information management l5.2 (2011):171-183. Web.13th July 2018. Online: https://www.tandfonline.com/doi/abs/10.1080/09737766.2011.1070091

Suresh Kumar, P.K, “Author productivity and the application of Lotka’s Law in LIS publications” Annals of Library and Information Studies 64.4 (2017):234-241. Web.14th March 2019. Online: http://nopr.niscair.res.in/handle/123456789/43426

Zafrunnisha, N."Citation Analysis of PhD Theses in Psychology of Selected Universities in Andhra Pradesh, India" Library Philosophy and Practice 735(2012). Web.20th Jan 2018. Online: http://digitalcommons.unl.edu/libphilprac/735