Jevin D. West

The Eigenfactor™ Metrics

Quantitative metrics are poor choices for assessing the research output of an individual scholar. Summing impact factors, counting citations, tallying an h-index, or looking at Eigenfactor™ Scores (described below)—none of these methods are adequate compared with what should be the gold standard

The Role of Gender in Scholarly Authorship

Gender disparities appear to be decreasing in academia according to a number of metrics, such as grant funding, hiring, acceptance at scholarly journals, and productivity, and it might be tempting to think that gender inequity will soon be a problem of the past. However, a large-scale analysis based on over eight million papers across the natural sciences, social sciences, and humanities reveals a number of understated and persistent ways in which gender inequities remain. For instance, even where raw publication counts seem to be equal between genders, close inspection reveals that, in certain fields, men predominate in the prestigious first and last author positions. Moreover, women are significantly underrepresented as authors of single-authored papers. Academics should be aware of the subtle ways that gender disparities can occur in scholarly authorship.

The Eigenfactor MetricsTM: A Network Approach to Assessing Scholarly Journals

Limited time and budgets have created a legitimate need for quantitative measures of scholarly work. The well-known journal impact factor is the leading measure of this sort; here we describe an alternative approach based on the full structure of the scholarly citation network. The Eigenfactor and Article Influence Score use an iterative ranking scheme similar to Googles PageRank algorithm. With this approach, citations from top journals are weighted more heavily than citations from lower-tier publications. We describe these metrics and the rankings that they provide.

Memory in network flows and its effects on spreading dynamics and community detection

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and although we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.

Assessing citations with the Eigenfactor™ Metrics

For more than 80 years, researchers and administrators alike have evaluated the prestige and productivity of researchers, institutions, journals, and even nations by counting citations.1 For the past half-century, the impact factor 2 has been the most prominent of these citation metrics. Impact factor is essentially a measure of the average number of citations that a journal’s articles receive over the two calendar years following publication. As a citation metric, impact factor has a number of virtues, not the least of which are that it is simple to describe and easy to calculate. But there are also drawbacks to the impact factor. In particular, impact factor does not account for differences in prestige among the citing journals3 and does not account for differences in citation patterns within and across disciplines.4 Thus in the impact factor calculation, a citation from Nature is worth no more than a …

Big macs and Eigenfactor scores

A recent article by Phil Davis suggested that the Eigenvalue metric does adds little useful information to the more simply calculated measure of total citations published by the ISI. This paper argues that Daviss claim is an instance of a classic statistical fallacy of spurious correlation. Based on an analysis of the entire 2006 ISI Journal Citation Reports, we show that there are statistically and economically significant differences between the Eigenfactor metrics and the ISIs impact factor and total citations.

Author-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community

In this article, we show how the Eigenfactor score, originally designed for ranking scholarly journals, can be adapted to rank the scholarly output of authors, institutions, and countries based on author‐level citation data. Using the methods described in this article, we provide Eigenfactor rankings for 84,808 disambiguated authors of 240,804 papers in the Social Science Research Network (SSRN)—a preprint and postprint archive devoted to the rapid dissemination of scholarly research in the social sciences and humanities. As an additive metric, the Eigenfactor scores are readily computed for collectives such as departments or institutions as well. We show that a collectives Eigenfactor score can be computed either by summing the Eigenfactor scores of its members or by working directly with a collective‐level cross‐citation matrix. We provide Eigenfactor rankings for institutions and countries in the SSRN repository. With a network‐wide comparison of Eigenfactor scores and download tallies, we demonstrate that Eigenfactor scores provide information that is both different from and complementary to that provided by download counts. We see author‐level ranking as one filter for navigating the scholarly literature, and note that such rankings generate incentives for more open scholarship, because authors are rewarded for making their work available to the community as early as possible and before formal publication.

A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network

The scholarly literature is expanding at a rate that necessitates intelligent algorithms for search and navigation.For the most part, the problem of delivering scholarly articles has been solved. If one knows the title of an article, locating it requires little effort and, paywalls permitting, acquiring a digital copy has become trivial. However, the navigational aspect of scientific search - finding relevant, influential articles that one does not know exist - is in its early development. In this paper, we introduce EigenfactorRecommends - a citation-based method for improving scholarly navigation. The algorithm uses the hierarchical structure of scientific knowledge, making possible multiple scales of relevance for different users. We implement the method and generate more than 300 million recommendations from more than 35 million articles from various bibliographic databases including the AMiner dataset. We find little overlap with co-citation, another well-known citation recommender, which indicates potential complementarity. In an online A-B comparison using SSRN, we find that our approach performs as well as co-citation, but this new approach offers much larger recommendation coverage. We make the code and recommendations freely available at babel.eigenfactor.organd provide an API for others to use for implementing and comparing the recommendations on their own platforms.

The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles

Despite growing interest in Open Access (OA) to scholarly literature, there is an unmet need for large-scale, up-to-date, and reproducible studies assessing the prevalence and characteristics of OA. We address this need using oaDOI, an open online service that determines OA status for 67 million articles. We use three samples, each of 100,000 articles, to investigate OA in three populations: (1) all journal articles assigned a Crossref DOI, (2) recent journal articles indexed in Web of Science, and (3) articles viewed by users of Unpaywall, an open-source browser extension that lets users find OA articles using oaDOI. We estimate that at least 28% of the scholarly literature is OA (19M in total) and that this proportion is growing, driven particularly by growth in Gold and Hybrid. The most recent year analyzed (2015) also has the highest percentage of OA (45%). Because of this growth, and the fact that readers disproportionately access newer articles, we find that Unpaywall users encounter OA quite frequently: 47% of articles they view are OA. Notably, the most common mechanism for OA is not Gold, Green, or Hybrid OA, but rather an under-discussed category we dub Bronze: articles made free-to-read on the publisher website, without an explicit Open license. We also examine the citation impact of OA articles, corroborating the so-called open-access citation advantage: accounting for age and discipline, OA articles receive 18% more citations than average, an effect driven primarily by Green and Hybrid OA. We encourage further research using the free oaDOI service, as a way to inform OA policy and practice.

Men Set Their Own Cites High: Gender and Self-citation across Fields and over Time:

How common is self-citation in scholarly publication, and does the practice vary by gender? Using novel methods and a data set of 1.5 million research papers in the scholarly database JSTOR published between 1779 and 2011, the authors find that nearly 10 percent of references are self-citations by a paper’s authors. The findings also show that between 1779 and 2011, men cited their own papers 56 percent more than did women. In the last two decades of data, men self-cited 70 percent more than women. Women are also more than 10 percentage points more likely than men to not cite their own previous work at all. While these patterns could result from differences in the number of papers that men and women authors have published rather than gender-specific patterns of self-citation behavior, this gender gap in self-citation rates has remained stable over the last 50 years, despite increased representation of women in academia. The authors break down self-citation patterns by academic field and number of authors and comment on potential mechanisms behind these observations. These findings have important implications for scholarly visibility and cumulative advantage in academic careers.