Fact-checked by Grok 2 weeks ago

Eigenfactor

The Eigenfactor is a journal ranking metric that evaluates the total influence of a scholarly journal within the global network of academic citations, using principles of network analysis to weight citations based on the prestige of the citing sources. Developed in 2007 by Jevin D. West and Carl T. Bergstrom at the University of Washington, it addresses limitations of traditional metrics like the Journal Impact Factor by excluding journal self-citations, incorporating a five-year citation window, and accounting for the varying sizes and interconnected prestige of journals in the scholarly literature. The metric produces two primary scores: the Eigenfactor Score, which quantifies a journal's overall contribution to the scientific community (scaled such that the sum across all journals equals 100), and the Article Influence Score, which normalizes the Eigenfactor by the number of articles published to yield an average per-article prestige value (with a mean of 1.00 across all journals). At its core, the Eigenfactor Score is computed using the leading eigenvector of a modified Markov transition matrix derived from citation data in the Thomson Reuters Journal Citation Reports (now Clarivate Analytics). This matrix represents the flow of scientific influence as a random surfer model—analogous to Google's PageRank—where citations from high-prestige journals carry more weight than those from less influential ones, and a damping factor simulates occasional random jumps to prevent over-reliance on tightly linked citation clusters. Self-citations are entirely omitted to avoid artificial inflation, and the analysis spans citations received in a given year to articles published in the preceding five years, providing a more stable and forward-looking assessment than the Impact Factor's typical two-year window. Unlike the Impact Factor, which simply averages citations per article without considering the source journal's quality or the network structure, the Eigenfactor emphasizes the broader value of a journal to researchers, such as the time researchers spend reading its articles. Since its introduction, the Eigenfactor has been integrated into tools like the Journal Citation Reports and freely available via the Eigenfactor website, enabling researchers, librarians, and institutions to better navigate scholarly publishing. It has influenced discussions on open access, journal prestige, and research evaluation by highlighting how citation patterns reveal the interconnected structure of science, though it remains one metric among many and is not without critiques regarding data dependencies on proprietary sources.

Definition and Purpose

Core Concept

The Eigenfactor score serves as a metric for assessing a scientific journal's total importance within the scholarly community, derived from eigenvector centrality applied to the global network of journal citations. This approach conceptualizes citations as weighted "votes" from one journal to another, where the prestige of the citing journal amplifies the value of its endorsements, thereby capturing the interconnected structure of scientific influence rather than merely aggregating raw citation counts. To compute the score, the method draws on a citation matrix spanning a five-year period of incoming references to recent articles, excluding self-citations by nullifying diagonal elements in the matrix to prevent artificial inflation from intra-journal referencing. The resulting scores are normalized such that the sum of Eigenfactor scores across all journals equals 100, providing a relative measure of each journal's share of the overall influence in the citation network. A related metric, the Article Influence Score, normalizes the Eigenfactor by the number of articles published in the journal, yielding a per-article estimate of influence scaled to an average of 1 across all journals.

Intended Applications

The Eigenfactor score is primarily utilized for ranking scholarly journals to inform decisions in academic libraries, personnel evaluations, and resource allocation. Academic librarians employ it to assess the value of journals relative to subscription costs, prioritizing those with high influence to optimize limited budgets for collections that maximize scholarly impact. In tenure and promotion processes, as well as funding decisions, Eigenfactor rankings help evaluate the prestige of journals where researchers publish, providing a network-based measure of a journal's contribution to scientific discourse that influences hiring, merit reviews, and grant assessments. Since , the Eigenfactor integrated into Clarivate's (JCR), making it accessible through the for comprehensive . This supports its use in evaluating journals across diverse fields, including interdisciplinary , by modeling flows that reveal between disciplines and highlighting influential outlets that traditional boundaries. For instance, in fields like or bioinformatics, where citations often multiple domains, Eigenfactor helps identify journals that serve as hubs in the broader . The Eigenfactor promotes its metrics as a means to reveal the underlying "structure of " via free online rankings and visualizations available at eigenfactor., enabling users to explore citation patterns and journal importance without proprietary access barriers. Unlike the simpler Journal Impact Factor, which focuses on average citations, Eigenfactor offers a more holistic for complex evaluations of journal .

History and Development

Origins and Creators

The Eigenfactor metrics were developed by Jevin D. West and Carl T. Bergstrom at the University of Washington, with significant contributions from Theodore C. Bergstrom, an economist at the University of California, Santa Barbara. The project originated from informal discussions in 2005 between West, a graduate student in biology, and Carl Bergstrom, an associate professor focusing on theoretical and evolutionary biology, as they sought better ways to evaluate scholarly impact using citation data. The term "Eigenfactor" itself emerged during these conversations, reflecting their aim to adapt network-based ranking concepts to academic publishing. This work was motivated by the recognized shortcomings of traditional metrics like raw citation counts and the Journal Impact Factor, which treat all citations equally regardless of the citing journal's prestige and fail to capture the interconnected structure of scientific influence. Drawing inspiration from Google's PageRank algorithm, the developers sought to weight citations by the influence of the source, leveraging eigenvector centrality as a mathematical foundation to quantify a journal's overall contribution to science. The project built on Bergstrom's prior research in evolutionary biology and information theory, where he explored how information flows in biological and social systems, extending these ideas to model citation networks as directed graphs. Initial publications outlining the approach appeared in 2007, including Bergstrom's article in College & Research Libraries News. Funded in part by the under SBE-0915005, the Eigenfactor was formally co-founded in 2007 and sponsored by the labs of West and Bergstrom at the . The first prototype was tested using ' data, with early applications focusing on biological journals to validate the metrics against established benchmarks in that .

Evolution and Integration

The Eigenfactor project was officially launched in January 2007 through the establishment of eigenfactor.org, an academic research initiative co-founded by Carl Bergstrom and Jevin West at the University of Washington to provide network-based metrics for evaluating scholarly journals. The first public rankings based on Eigenfactor scores were released in 2008, utilizing citation data from the Thomson Reuters Journal Citation Reports (JCR) to rank journals by their influence within the scientific citation network. In 2009, Eigenfactor metrics were integrated into the JCR, marking a significant milestone in their adoption by a major bibliometric database provider and enabling broader accessibility through Thomson Reuters' (now Clarivate Analytics) platform. This incorporation allowed Eigenfactor scores to be calculated and disseminated annually alongside traditional metrics like the Journal Impact Factor, with coverage expanding to include more than 9,100 journals by the 2009 JCR release. By 2010, the dataset had grown further, reflecting the increasing scale of indexed periodicals and supporting more comprehensive network analyses. Eigenfactor scores undergo recalculations using the latest JCR , ensuring metrics reflect scholarly patterns. As of the 2025 JCR , these computations encompass 22,249 journals from 111 across 254 categories, with over 6,200 published under models, incorporating adjustments to for diverse practices and . A distinctive feature of the Eigenfactor project is its commitment to open science, providing free public access to historical raw ranking data up to 2015, interactive visualizations of citation networks, and tools like journal maps on eigenfactor.org to facilitate transparent research and exploration of scientific structures, while current scores are available through the Journal Citation Reports.

Methodology

Citation Data and Network Construction

The Eigenfactor metrics rely on comprehensive citation data sourced from Clarivate's Journal Citation Reports (JCR), which is derived from the Web of Science database. This source encompasses over 22,000 journals across scientific and social scientific disciplines, capturing millions of citations to provide a broad representation of scholarly communication. The is modeled as a directed, weighted , with journals serving as nodes and directed edges representing the flow of citations from citing journals to cited journals. Citation counts are aggregated over a five-year window to reflect contemporary influence patterns, ensuring the network captures dynamic scholarly interactions without overemphasizing historical data. During preprocessing, citations are aggregated at the journal level to focus on collective impact rather than individual articles, streamlining the analysis for large-scale computation. Self-citations, where a journal cites its own articles, are systematically excluded by assigning them a weight of zero; this adjustment mitigates potential biases from reciprocal citing practices within the same publication. This network structure uniquely incorporates the global interconnectedness of journals, allowing influence to propagate through multi-step citation chains—for example, the impact of a journal can be amplified if it is cited by highly influential intermediaries that cite the target journal. Such propagation draws on principles akin to eigenvector centrality, enabling a holistic assessment of a journal's position within the broader scholarly ecosystem.

Eigenfactor Score Computation

The Eigenfactor Score quantifies a journal's total importance within the scholarly citation network by leveraging eigenvector centrality, treating citations as directed links that propagate influence iteratively. This approach draws from network theory, where the score reflects the steady-state distribution of a random surfer navigating the citation graph, emphasizing the prestige of citing sources. Specifically, the computation relies on a normalized citation matrix derived from aggregated journal-level data, ensuring that the influence of citations is weighted by the authority of the originating journal. To derive the score, first construct the normalized citation matrix M, where M_{ij} represents the proportion of citations from journal i to journal j, defined as M_{ij} = \frac{c_{ij}}{c_i}. Here, c_{ij} denotes the total citations from articles in journal i to articles in journal j, and c_i = \sum_j c_{ij} is the total outgoing citations from journal i. This row-stochastic matrix M (where each row sums to 1) models transition probabilities in the citation network, excluding self-citations to avoid artificial inflation. The matrix incorporates only citations received by articles published within a 5-year window prior to the census year, capturing recent scholarly impact while mitigating biases from aging literature. For instance, in calculations using 2006 Journal Citation Reports data, citations target articles from 2001–2005. Journals with no outgoing citations (dangling nodes) are handled by replacing their corresponding rows in the (or equivalent) with the a, where a_i is the proportion of total articles published by journal i over the five-year window. A \alpha = 0.85 is then applied to form the P = \alpha H' + (1 - \alpha) a \mathbf{e}^T, where H' is the adjusted normalized and \mathbf{e} is a of ones. This incorporates random jumps proportional to journal publication volume to ensure convergence and handle network structure. The Eigenfactor Score for journal j, denoted EF_j, is derived from the principal eigenvector \mathbf{v} of P, scaled such that the sum across all journals equals 100: EF_j = 100 v_j. This eigenvector captures the long-term influence, as a citation from a high-influence journal (high v_i) contributes more to the score of the cited journal than one from a low-influence source, with weights propagating recursively through the network—akin to how prestige accrues in interconnected systems. To solve for \mathbf{v}, the power method is employed: initialize a uniform vector \mathbf{v}^{(0)} = (1/n, \dots, 1/n) for n journals, then iterate \mathbf{v}^{(k+1)} = \mathbf{v}^{(k)} P until convergence (typically when \|\mathbf{v}^{(k+1)} - \mathbf{v}^{(k)}\| < \epsilon, with \epsilon \approx 10^{-5}), and normalize so the sum equals 1. The resulting scores sum to 1 across all journals before scaling to percentages by multiplying by 100. This iterative process converges linearly for irreducible matrices, ensuring computational feasibility even for large networks of thousands of journals.

Article Influence Score

The Article Influence Score (AIS) serves as a normalized measure of the average influence per article in a , providing a per-article counterpart to the total journal influence captured by the Eigenfactor Score. It addresses the size inherent in aggregate metrics by apportioning a journal's overall across its published articles, thus enabling fairer comparisons between journals of varying output volumes. Introduced alongside the Eigenfactor Score in 2007, the AIS was developed to complement network-based citation analysis with a metric focused on individual impact, drawing on the same five-year citation window used in Eigenfactor calculations. The AIS is computed by dividing the journal's Eigenfactor Score by the total number of articles it published in the preceding five years, then scaling the result by a constant factor of 0.01 to ensure the mean score across all journals in the Journal Citation Reports is 1.0. This yields the formula: \text{AIS} = \frac{\text{Eigenfactor Score}}{\text{Number of Articles (prior 5 years)}} \times 0.01 The scaling ensures interpretability, where an AIS of 1.0 represents the average influence per article across the indexed literature. In interpretation, an AIS greater than 1.0 signifies above-average per-article influence relative to the global , while scores below 1.0 indicate below-average ; for example, a score of 9.63 for in placed it in the top 0.5% of journals by per-article . Unlike the traditional Journal Impact Factor, which relies on simple citation counts, the AIS incorporates effects from the Eigenfactor , weighting citations by the of citing journals to better reflect substantive scholarly .

Comparisons and Alternatives

Relation to Journal Impact Factor

The Journal Impact Factor (IF), developed by Eugene Garfield and published in Journal Citation Reports, is calculated as the average number of citations received in a given year by articles published in that journal during the previous two years, treating all citations equally regardless of the citing source. In contrast, the Eigenfactor score employs a five-year citation window rather than two years, providing a longer-term assessment of influence. Unlike the IF, which includes self-citations in its tally, the Eigenfactor explicitly discounts them by excluding references from the same journal in the citation matrix. Furthermore, while the IF relies on flat citation counts without regard to the prestige of the citing journal, the Eigenfactor incorporates a network-based weighting that amplifies citations from more influential sources, akin to Google's PageRank algorithm. For instance, a journal that receives citations primarily from high-prestige outlets will see its Eigenfactor score elevated due to the recursive prestige , whereas the same citation would not alter its IF beyond the raw count. The Article Influence Score, derived from the Eigenfactor, serves as a per-article analog to the IF by normalizing the Eigenfactor by the number of articles published. A distinctive feature of the Eigenfactor is its proportional scaling, where the scores across all journals sum to 100, reflecting relative influence within the scholarly network; in comparison, IF values are absolute and can vary widely by field without such normalization.

Strengths Over Traditional Metrics

The Eigenfactor score addresses key limitations of traditional metrics like the Journal Impact Factor (IF) by leveraging a network-based model that weights citations according to the influence of the citing journal, thereby reducing biases from self-citations and disproportionate advantages for large journals. Unlike the IF, which counts all incoming citations equally regardless of source, the Eigenfactor explicitly excludes self-citations to prevent artificial inflation of scores and iteratively propagates influence through the citation graph, diminishing the impact of citations from peripheral or low-prestige outlets. This approach ensures that a journal's score reflects genuine external validation rather than volume-driven or insular referencing patterns. By modeling citations as a directed network, the Eigenfactor captures indirect influence via citation chains, where the prestige of a citation from a highly influential journal enhances the value of subsequent citations it inspires. This contrasts with the IF's reliance on direct, unweighted citations within a narrow two-year window, providing a broader assessment of a journal's sustained role in advancing knowledge across the scientific community. For example, in analyses of top-cited journals, the Eigenfactor elevates publications like the Journal of Biological Chemistry, which may appear undervalued under IF due to citation distribution patterns but demonstrate substantial network centrality. The Eigenfactor's enables implicit , for disparate rates and volumes across disciplines without requiring separate calculations per . This makes it particularly for interdisciplinary comparisons, where the IF often favors high-citation fields like over with sparser referencing norms, such as sciences. Through its and eigenvector , the balances proportionally to output, promoting equitable . Empirical evidence underscores these strengths, with studies showing Eigenfactor scores exhibiting stronger correlations with overall citation influence (e.g., total citations received) than the IF, reflecting a more accurate portrayal of journal prestige. In fields like biology, where citation networks are dense and interconnected, the Eigenfactor aligns more closely with perceived journal quality, as seen in rankings that better match expert-informed hierarchies compared to IF-based lists. This network perspective fosters a "democratized" assessment, prioritizing the substantive quality and interconnected impact of citations over raw quantity, thereby supporting fairer resource allocation in academia.

Criticisms and Limitations

Methodological Concerns

One key methodological concern with the Eigenfactor score stems from its reliance on citation data from the Web of Science (WoS) Core Collection, which exhibits significant coverage biases that underrepresent non-English-language journals and those in the social sciences. For instance, WoS prioritizes English-dominant publications from Western countries, leading to incomplete networks for global or humanities-oriented scholarship and potentially skewing influence scores toward established, English-language outlets in STEM fields. This data dependency limits the metric's applicability in diverse academic landscapes, as Eigenfactor cannot incorporate citations from underrepresented sources like regional databases or non-indexed journals. The fixed 5-year citation window used in Eigenfactor computations also raises issues, particularly in fields with varying citation tempos, where it may undervalue journals in fast-moving disciplines. In rapidly evolving areas like , citations peak early (often within 2 years) and decline sharply, whereas slower fields such as see delayed peaks around 3 years with prolonged tails; the 5-year aggregation thus disproportionately benefits the latter, altering relative journal rankings across disciplines. This temporal uniformity overlooks disciplinary differences in knowledge dissemination speeds, potentially misrepresenting influence in dynamic sectors like or . Within the citation network model, Eigenfactor treats all incoming citations as equivalent positive signals of influence, akin to PageRank's hyperlink assumptions, without accounting for citation intent or context. This overlooks negative citations—such as critiques or retractions—that do not enhance scholarly value but still contribute to the score, inflating for controversial work. Additionally, the metric's to (JCR) category assignments exacerbates aggregation errors, as journals are grouped into schemas that misalign with nuanced subfields, leading to distorted rankings. Early implementations of Eigenfactor to 2010 suffered from pronounced aggregation errors in handling multidisciplinary journals, where category schemas failed to disambiguate cross-field citations, resulting in inconsistent and incomplete representations; subsequent updates partially mitigated these through refined JCR integrations, though inaccuracies persist in interdisciplinary contexts. Finally, validation of Eigenfactor's reliability is hampered by a scarcity of longitudinal studies examining score stability over extended periods, with available analyses indicating relative consistency in select medical journals but lacking broad, multi-decade assessments across disciplines to confirm robustness against data fluctuations.

Broader Impacts on Academia

The use of Eigenfactor scores in academic evaluations has significantly influenced key decisions in hiring, promotion, tenure, and funding allocations, often prioritizing publications in high-scoring journals over other forms of scholarly output. This reliance can lead researchers to favor submissions to top-ranked journals, potentially discouraging contributions to lower-ranked or specialized outlets that may better serve niche communities or emerging fields. Such patterns exacerbate inequalities by concentrating resources and prestige among a select group of publications, mirroring concerns raised about the Journal Impact Factor. Critics, including Eigenfactor's own developers, have highlighted how journal ranking systems contribute to overemphasis on metrics that distorts research priorities and reduces diversity in publishing. Studies from 2007 to 2015 by creators Carl Bergstrom and colleagues warned against using such quantitative measures for individual assessments, noting their potential to undervalue interdisciplinary or innovative work outside high-impact venues. A 2013 analysis by Larivière, Gingras, and Sugimoto demonstrated that journal ranks, including those akin to Eigenfactor, create feedback loops that steer submissions away from lower-ranked journals, thereby homogenizing scientific output and limiting the breadth of accessible knowledge. As of 2025, these issues remain central to ongoing ethical debates, with the Declaration on Research Assessment ()—now endorsed by over ,500 organizations—explicitly recommending against over-reliance on journal-based metrics like Eigenfactor for evaluating research quality or individual performance. advocates for holistic assessments that consider societal impact, , and contributions beyond citation counts to mitigate systemic biases and promote equitable academia.

References

  1. [1]
    [PDF] EigenfactorTM Score and Article Influence TM Score
    Here we describe the methods used to compute the EigenfactorTM scores and Article InfluenceTM scores featured at www.eigenfactor.org1. The.
  2. [2]
    The Eigenfactor™ Metrics - Journal of Neuroscience
    Nov 5, 2008 · The Eigenfactor Score is a measure of the journal's total importance to the scientific community; if a journal doubles in size while the quality ...Missing: original | Show results with:original
  3. [3]
    About - Eigenfactor
    Article Influence scores are normalized so that the mean article in the entire Thomson Journal Citation Reports (JCR) database has an article influence of 1.00.
  4. [4]
    A Closer Look at the Eigenfactor™ Metrics | Clarivate
    May 23, 2017 · The difference is that the Eigenfactor Score measures the total influence of a journal, and the Article Influence Score takes into account the ...What is the difference between... · What is the new Normalized...
  5. [5]
    Subscription Cost Effectiveness - Eigenfactor
    The Eigenfactor subscription cost tool tracks subscription costs of scholarly journals and relates these to measures of journal influence.Missing: intended applications
  6. [6]
    [PDF] COST EFFECTIVENESS OF OPEN ACCESS PUBLICATIONS
    Jul 1, 2014 · A record of publication in the top tiers of the journal hierarchy has a critical impact on hir- ing, promotion, tenure, merit, salary, and ...
  7. [7]
    The Eigenfactor Metrics: A network approach to assessing scholarly ...
    Jul 16, 2009 · The Eigenfactor and Article Influence Score use an iterative ranking scheme similar to Google's PageRank algorithm.Missing: original | Show results with:original
  8. [8]
    The Eigenfactor™ Metrics - PMC - PubMed Central - NIH
    The Eigenfactor Score is a measure of the journal's total importance to the scientific community; if a journal doubles in size while the quality of its articles ...Missing: definition | Show results with:definition
  9. [9]
    Eigenfactor: Revealing the Structure of Science
    Carl Bergstrom and Jevin West each presented work from the Eigenfactor project at the International Symposium on Science of Science this week in Washington, DC.Journal Ranking · About · Papers · Scholarly PublishingMissing: original | Show results with:original
  10. [10]
    [PDF] Eigenfactor: ranking and mapping scientific knowledge - Jevin West
    ' Carl Bergstrom and I came up with the word 'Eigenfactor' back in 2005 over conversations on how to better evaluate scholarly work. Carl was in- terested in ...
  11. [11]
    The Eigenfactor MetricsTM: A Network Approach to Assessing ...
    The Eigenfactor Metrics—Eigenfactor Score and Article Influence Score—use an iterative ranking scheme similar to Google's PageRank algorithm. By this approach, ...Missing: original | Show results with:original<|separator|>
  12. [12]
    Eigenfactor: Measuring the value and prestige of scholarly journals
    Eigenfactor: Measuring the value and prestige of scholarly journals. Carl Bergstrom. Full Text: PDF. DOI: https://doi.org/10.5860/crln.68.5.7804Missing: original | Show results with:original
  13. [13]
    [PDF] special interest - Carl T. Bergstrom
    The Eigenfactor Project began as an attempt to better evaluate the scholarly literature, using citation data and powerful tools from network and information ...<|control11|><|separator|>
  14. [14]
    Eigenfactor Journal Ranking Search
    Journal Ranking. Journal Name. ISSN. Publisher. 2015, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014.
  15. [15]
    The most influential journals: Impact Factor and Eigenfactor - PMC
    Apr 28, 2009 · The Eigenfactor™ is now listed by Journal Citation Reports®. In practice, there is a strong correlation between Eigenfactors and the total ...
  16. [16]
    Thomson Reuters Releases Journal Citation Reports for 2009
    Jun 17, 2010 · In the 2009 JCR release, 1,055 journals receive their first calculated Journal Impact Factors. Eigenfactor™ Metrics that assess a journal's ...Missing: integration | Show results with:integration
  17. [17]
    2009 Journal Citation Reports Released - Wordpress + Temple
    Jun 23, 2010 · More than 850 regional journals included as part of Regional Content Expansion initiative; 1,055 journals with their first published Journal ...
  18. [18]
    Clarivate Unveils the 2025 Journal Citation Reports
    Jun 18, 2025 · The JCR includes data from a total of 22,249 journals across 254 categories · Over 6,200 of these were published via gold open access ...Missing: Eigenfactor | Show results with:Eigenfactor
  19. [19]
    [PDF] The Eigenfactor™ Metrics - Semantic Scholar
    The Eigenfactor™ Metrics · Carl T. Bergstrom, Jevin D. West, Marc A Wiseman · Published in Journal of Neuroscience 5 November 2008 · Economics.Missing: seminal | Show results with:seminal
  20. [20]
    The most influential journals: Impact Factor and Eigenfactor - PNAS
    Apr 28, 2009 · Three journals have by far and away the most overall influence on science: Nature, PNAS, and Science, closely followed by the Journal of Biological Chemistry.
  21. [21]
    Rethinking the Journal Impact Factor and Publishing in the Digital Age
    The Eigenfactor Metrics: A network approach to assessing scholarly journals. UC Santa Barbara: Department of Economics site. 2010. https://escholarship.org ...
  22. [22]
    The Social Science Citation Index: A Black Box—With an Ideological ...
    Aug 8, 2025 · ... eigenfactor, article influence score, and SCImago journal rank. ... no Chinese-medium SSCI journal (Web of Science Group 2020). ... The ...
  23. [23]
    [PDF] How Impact Factor and Other Metrics Differ across Disciplines
    This study addresses the following questions: How well represented are different disciplines in the indexing of each metrics system (Eigenfactor, Scopus, Web of ...
  24. [24]
    [PDF] A Network Approach to Assessing Scholarly Journals
    Jevin D. West is a Ph.D. student in the Department of Biology at University of Washington; e-mail: jevinw@u.washington.edu. Theodore C. Bergstrom is ...Missing: creators | Show results with:creators
  25. [25]
    [PDF] 4.7 Eigenfactor - arXiv
    Abstract: The Eigenfactor™ is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington.Missing: original paper
  26. [26]
    Evaluating Journal Impact Factor: a systematic survey of the pros ...
    Aug 31, 2020 · Other citation metrics, such as Eigenfactor and Article influence score, are less prone to the inflation practice of self-citation, providing ...
  27. [27]
    The problems with the subject categories schema in the EigenFactor ...
    Sep 21, 2012 · This paper aims to examine the quality of the subject categories created for EF‐2010 and of the JCR‐2010 subject categories as implemented ...Missing: concerns | Show results with:concerns
  28. [28]
    A Longitudinal Rate of Change Analysis Using Mixed-Effects Models
    Jul 21, 2025 · In contrast, alternative metrics such as the Eigenfactor Score and Article Influence Score remained relatively stable across the same period.
  29. [29]
    Eigenfactor score and alternative bibliometrics surpass the impact ...
    A re-ranking of journals using Eigenfactor Score, Article Influence Score, and Cited Half-life provides a better assessment of their significance and importance ...
  30. [30]
    Index of Open Access Fees - Eigenfactor
    As we all know, a record of publications in the top tiers of the journal hierarchy has a critical impact on hiring, promotion, tenure, merit, and funding ...Missing: intended applications
  31. [31]
    Deep impact: unintended consequences of journal rank - PMC
    ... expert ratings or retractions). These data corroborate ... Correlation between journal impact factor and citation performance: an experimental study.
  32. [32]
    Causes for the Persistence of Impact Factor Mania - PubMed Central
    Mar 18, 2014 · We conclude that impact factor mania persists because it confers significant benefits to individual scientists and journals.
  33. [33]
    Read the Declaration | DORA
    The Declaration on Research Assessment (DORA) recognizes the need to improve the ways in which the outputs of scholarly research are evaluated.
  34. [34]
    San Francisco Declaration on Research Assessment (DORA)
    The Declaration on Research Assessment (DORA) recognizes the need to improve the ways in which researchers and the outputs of scholarly research are evaluated.Resource Library · Read · Signers · A decade of DORA | DORA