Regional profile of questionable publishing

TL;DR

The paper investigates regional patterns in questionable publishing by constructing a paired set of questionable and unquestioned journals with comparable impact and analyzing author-country affiliations from a large Scopus dataset (2010–2018). It assigns papers to the first author's country and defines a journal's publishing country, then uses two types of neighboring relationships (geographical and academic) to assess regionality. The findings show that while low-GDP countries appear to contribute more to questionable publishing in raw counts, the rate of questionable publishing is weakly related to GDP, and high-GDP countries contribute more in absolute terms due to larger overall output; questionable journals largely attract authors from a broad set of countries rather than serving as regional platforms. The study concludes that questionable journals do not meaningfully sustain regional academia, notes biases and limitations in Beall's list, and discusses implications for evaluating and understanding the globalization of scholarly publishing.

Abstract

Countries and authors in the academic periphery occasionally have been criticized for contributing to the expansion of questionable publishing because they share a major fraction of papers in questionable journals. On the other side, topics preferred by mainstream journals sometimes necessitate large-scale investigation, which is impossible for developing countries. Thus, local journals, commonly low-impacted, are essential to sustain the regional academia for such countries. In this study, we perform an in-depth analysis of the distribution of questionable publications and journals with their interplay with countries quantifying the influence of questionable publications regarding academia's inequality. We find that low-impact journals play a vital role in the regional academic environment, whereas questionable journals with equivalent impact publish papers from all over the world, both geographically and academically. The business model of questionable journals differs from that of regional journals, and may thus be detrimental to the broader academic community.

Figures (12)

• Figure 1: Country-specific publication statistics for questionable and unquestionable journals. The diameter of the circle represents the country's total number of publications, whereas its hue represents the GDP in 2018. Countries that published fewer than 100 papers are excluded from the visualization. (A) Countries' proportion of questionable and unquestionable publications among their entire publications. Countries with a high GDP publish a lower proportion of questionable journals, whereas countries with a low GDP publish a greater proportion of questionable journals. (B) The number of questionable and unquestioned publications by country. Countries with a high GDP publish more in both questionable and unquestionable journals than those with a low GDP. The majority of countries are arranged in a diagonal line, indicating that they generate a comparable number of publications in both questionable and unquestionable journals.
• Figure 2: Distribution of journals based on the proportion of publications from three different definitions of neighboring countries.(A) The publication rate of the primary country in the journal. Here, the primary country is the first author's country that has the largest share of a given journal. (B) Publication proportion for geographical neighbors. (C) Publication proportion for academic neighbors. To minimize noise, each figure is rendered using the KDE plot.
• Figure 3: The rank-ordered average share of a country's journal publications based on the primary country's publication rate. (A) The colorbar represents the publication rate of the primary country in the journal. For journals that the primary country's share is high (red), the publication share of other countries is low. For journals in which the primary country's share is low (blue), various countries with lower ranks publish more papers. (B) Publishing preference distribution for each of the three groups. The first group published more in journals with low shares of primary countries (left), while the countries in the second group published comparable percentages in journals with both high and low shares of main countries (center). The third group publishes more in journals that have a large proportion of primary countries (right). The error bars represent the standard error. (C) Countries clustered by the distribution of journals' primary countries share profiles. The journals are divided into ten classes based on their share of the primary country in (A) and estimate the portion of each class inside the country (Figure \ref{['fig:sm_country_primary_dist']}). We then cluster the countries into three groups based on the estimated portions by Ward's method ward1963hierarchical. Note that we excluded countries with fewer than 10 journals for the analysis.
• Figure 4: Heatmap comparing the primary country's publication share to the journal's impact. The heatmaps depict the normalized frequency of questionable, unquestionable, and other journals, along with the proportion of the primary country, geographical neighbor, and academic neighbors, as well as the impact of the journal. The red area denotes a dense concentration of journals. The color bar represents the normalized density, which is normalized by maximal density of each plot.
• Figure S1: The proportion of publications by country compared to GDP. GDP and proportion of publications are strongly correlated (0.967 in 2010 and 0.972 in 2018). The solid regression line displays the fitted model of $y \sim x^{1.2}$, where $x$ is GDP and $y$ is % of publications. Top 10 countries produces more than 60% of publications each year.