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Impact factor

The journal impact factor (JIF), also known as the impact factor, is a bibliometric metric calculated annually by Clarivate that quantifies the average number of citations received by citable items—such as research articles and reviews—published in a scholarly journal during the two preceding years, divided by the total number of such citable items published in those years.^[1] Introduced as a tool for journal selection in abstracting and indexing services, the JIF has become a proxy for assessing journal prestige within academic fields, though its application extends to evaluating researchers, departments, and funding decisions despite methodological limitations.^[2] Devised by Eugene Garfield, founder of the Institute for Scientific Information (ISI), the concept of the impact factor emerged in the mid-1950s amid efforts to develop citation-based tools for navigating scientific literature, with Garfield first proposing it in a 1955 Science article as a means to rank journals by citation frequency.^[3] The formula specifically computes, for a given year y, the ratio of citations in y to recent journal content (from y-1 and y-2) over the number of citable publications in those prior years, excluding self-citations in the numerator to varying degrees based on Clarivate's policies.^[4] Initially published in ISI's Journal Citation Reports starting in 1975, the metric gained traction with the growth of Web of Science indexing, but its two-year window favors fast-citing disciplines like biomedicine over slower fields such as mathematics.^[2] While the JIF enables rough comparisons of journal influence within similar domains, it faces substantial criticism for conflating journal-level aggregates with individual article quality, encouraging citation inflation through practices like excessive self-citation or coordinated quoting, and distorting research priorities toward high-impact outlets at the expense of innovative or interdisciplinary work.^[5] Empirical studies reveal that JIF correlates imperfectly with actual citation distributions, where a minority of articles drive most citations, rendering the average misleading as a quality indicator.^[6] In response to these flaws, institutions like the Declaration on Research Assessment (DORA) advocate decoupling evaluations from JIF, emphasizing that overreliance perpetuates systemic biases in academic hiring and promotion, often prioritizing metrics over substantive contributions.^[7]

History

Origins and invention

The concept of the impact factor originated with Eugene Garfield's proposal in 1955 to use citation counts as a quantitative measure of journal influence, articulated in his article "Citation Indexes for Science: A New Dimension in Documentation Through Association of Ideas," published in Science.^[8] Garfield envisioned citation indexing not only for tracing idea associations but also for evaluating journals' relative importance based on how frequently their articles were referenced by subsequent publications, addressing the limitations of subjective assessments in an era of burgeoning scientific output.^[9] This idea laid the groundwork for the Science Citation Index (SCI), which Garfield's Institute for Scientific Information (ISI) launched in 1964 as the first comprehensive tool for systematic citation tracking across thousands of journals.^[10] The SCI enabled the practical computation of citation-based metrics by compiling references from peer-reviewed articles, initially covering approximately 600 journals selected through preliminary impact assessments developed by Garfield and collaborator Irving H. Sher in the early 1960s.^[9] The invention responded to the post-World War II explosion in scientific publishing, where annual journal output surged from thousands to tens of thousands of titles, complicating library acquisitions, researcher literature searches, and decisions on which periodicals merited indexing or subscription amid limited resources.^[11] Garfield argued that citation frequency provided an objective proxy for a journal's utility and prestige, reducing reliance on reputational heuristics or circulation figures that often favored established but less impactful outlets.^[3]

Early adoption and standardization

The Journal Citation Reports (JCR), introduced in 1975 by the Institute for Scientific Information (ISI, now part of Clarivate), marked the initial standardization of journal impact factors by compiling citation data from the Science Citation Index (SCI) to calculate and rank impact metrics for covered journals using 1974 citations.^[12] This annual publication formalized impact factors as a quantifiable measure of journal citation influence, initially serving librarians and publishers in evaluating journal value for subscriptions and collection development amid the post-World War II expansion of scientific literature.^[13] The methodology aggregated citations to recent citable items, providing transparent rankings that shifted journal assessment from subjective prestige to empirical citation patterns.^[14] Following its launch, JCR integrated impact factors into early scientific workflows, particularly for journal selection and inter-journal comparison within natural sciences, as ISI's SCI database covered over 2,000 journals by the mid-1970s.^[12] By the late 1970s, the inclusion of data from the Social Sciences Citation Index (SSCI, initiated in 1973) extended standardized impact factor calculations to approximately 1,400 social science journals, broadening applicability beyond physical and life sciences while relying on ISI's unified citation indexing framework.^[15] This expansion facilitated cross-disciplinary benchmarking, though humanities coverage remained limited to the separate Arts & Humanities Citation Index without routine impact factors until much later.^[12] As academic output surged in the 1980s, JCR's impact factors gained traction in institutional decisions, including resource allocation for libraries facing budget constraints and the proliferation of serials, which exceeded 100,000 titles globally by 1990.^[16] Their adoption standardized journal evaluation in workflows, reducing reliance on anecdotal reputation and enabling data-driven choices in an era of increasing specialization, though initial use emphasized practical utility over formal academic assessments.^[13]

Key developments and expansions

The proliferation of online bibliographic databases in the 1990s, such as the transition of the Science Citation Index to digital formats, facilitated greater access to literature and resulted in a marked increase in overall citation volumes, which in turn elevated journal impact factors across disciplines.^[17] This digital shift correlated with the emergence of open access initiatives and expanded indexing, contributing to higher citation rates but also prompting early concerns about inflationary pressures on the metric.^[18] By the mid-1990s, average impact factors had begun trending upward, reflecting broader dissemination of research but complicating cross-temporal comparisons.^[19] To mitigate challenges in comparing journals across disparate fields with varying citation norms, the Journal Citation Reports (JCR) emphasized subject category assignments, enabling within-discipline rankings and normalization of impact factors.^[20] These categories, refined through the 1990s, grouped journals by topical alignment, such as partitioning multidisciplinary outlets to prevent apples-to-oranges evaluations between high-citation fields like biomedicine and lower-citation humanities.^[15] This structural adaptation addressed inherent cross-disciplinary biases in raw impact factor values, promoting more equitable assessments for journal selection and evaluation.^[21] In the early 2000s, amid growing scrutiny over potential misapplications, Eugene Garfield, the metric's originator, reiterated its intended role as a journal-level tool rather than for individual articles or careers, defending it against charges of oversimplification while acknowledging limitations like self-citation inflation.^[3] ^[9] Concurrently, impact factors gained traction in national research evaluations, such as the UK's Research Assessment Exercise (RAE), where they served as supplementary indicators alongside peer review in exercises from 1996 onward, influencing institutional funding allocations despite debates over their supplementary status.^[22] ^[23] These integrations highlighted the metric's expanding influence, though they also intensified discussions on its robustness for policy decisions.^[24]

Calculation

Core methodology and formula

The journal impact factor (JIF) quantifies the frequency with which the average citable item in a journal is cited, using a precise ratio based on recent citations. For year y, it is calculated as the total number of citations in y to citable items published in the journal during y-1 and y-2, divided by the total number of such citable items published in those two years: \text{IF}_y = \frac{\text{Citations}_y \text{ to items from } y-1 \text{ and } y-2}{\text{Citable items published in } y-1 \text{ and } y-2} This two-year window prioritizes citations to recent substantive content, reflecting short-term influence rather than long-term legacy. Citable items are restricted to full-length, peer-reviewed research articles and reviews, which are deemed capable of attracting meaningful scholarly engagement; non-citable formats such as editorials, short communications, letters, corrections, and news notes are excluded from the denominator to avoid diluting the metric with low-citation-potential material.^[25]^[1] Clarivate performs the computation annually, drawing exclusively from citation and publication data indexed in the Web of Science Core Collection, ensuring consistency across journals. The resulting JIF values are released mid-year via the Journal Citation Reports (JCR); for instance, the 2023 JIF—based on 2023 citations to citable items from 2021 and 2022—was published in June 2024. An illustrative calculation for a high-impact journal might yield: This yields 41.577 for that year, highlighting how large citation volumes relative to publication output drive elevated scores.^[1]

Data sources and eligibility criteria

The impact factor relies exclusively on citation data sourced from Clarivate's Web of Science Core Collection, which indexes approximately 21,000 peer-reviewed journals across sciences, social sciences, arts, and humanities as of 2023.^[26] This database serves as the foundational repository for Journal Citation Reports (JCR), ensuring that only resolved citations—typically linked via DOIs or parsed bibliographic references—are counted toward the numerator of the impact factor formula.^[27] Non-resolved or unparsable references from non-indexed sources are excluded to maintain data integrity and verifiability.^[28] Journals qualify for inclusion in the Web of Science Core Collection through a rigorous editorial selection process emphasizing basic publishing standards, such as regular and timely issuance of issues, adherence to international ethical guidelines (e.g., COPE standards), and provision of English-language abstracts and keywords for global accessibility.^[26] Editorial content must demonstrate peer review rigor, with articles exhibiting original scholarly value and relevance to the journal's scope, excluding proceedings papers or non-substantive items unless they meet citable document criteria (primarily research articles and reviews).^[26] Selection further prioritizes editorial board quality and international diversity, requiring that a majority of board members possess verifiable publication records in Web of Science-indexed journals and exhibit broad citation networks spanning multiple countries to mitigate regional biases.^[26] Geographic representation among authors, editors, and cited works is assessed to ensure the journal's influence extends beyond national boundaries, with undue concentration in self-referential citation patterns (e.g., excessive journal self-citations exceeding 20-30% thresholds in monitored cases) potentially leading to exclusion or suppression from JCR rankings.^[29] Citation analysis during evaluation verifies the journal's external impact, confirming that references are drawn from a diverse pool of high-quality sources rather than promotional stacking or isolated networks.^[30] Only journals meeting these multifaceted criteria contribute to impact factor computations, with ongoing re-evaluation to uphold standards against practices like coercive citation requests.^[26]

Recent methodological updates

In response to evolving publishing practices, Clarivate incorporated early access (online-ahead-of-print) articles into the Journal Impact Factor (JIF) denominator starting with the 2021 release of Journal Citation Reports (JCR), with full implementation reflected in subsequent years including 2022, to better capture contemporary citation dynamics and reduce discrepancies between print and digital publication timelines.^[31]^[32] This adjustment addressed empirical evidence that excluding such content artificially inflated metrics for journals relying on rapid online dissemination, thereby enhancing the accuracy of the two-year citation window.^[33] A significant integrity-focused refinement occurred in the 2025 JCR release (based on 2024 data), where Clarivate announced the exclusion of citations to and from retracted articles in the JIF numerator, while retaining retracted items in the denominator to preserve transparency and accountability in publication counts.^[34]^[35] This change, effective from May 2025, responds to documented increases in retractions and aims to prevent invalid citations from distorting journal-level impact assessments, without altering the core formula's structure.^[36]^[37] Clarivate has also implemented ongoing procedural safeguards for journal title changes, mergers, and splits, requiring unification of metrics across the affected titles for up to two years post-event to deter strategic manipulations that could artificially boost or suppress JIF values.^[1]^[38] These policies, refined based on historical data from journal transitions, ensure continuity in evaluation while maintaining methodological consistency across evolving publication landscapes.^[39]

Applications

Journal ranking and selection

Impact factors enable the categorization of journals into quartiles (Q1–Q4) within specific subject areas, where Q1 denotes the top 25% by citation impact, Q2 the next 25%, Q3 the following quartile, and Q4 the bottom 25%.^[40]^[41] This system, derived from Journal Citation Reports data, facilitates relative prestige assessments by normalizing for field-specific citation practices, allowing selectors to identify high-performing outlets amid thousands of titles.^[42] For instance, in multidisciplinary sciences, journals like Nature achieve Q1 status with impact factors exceeding 50, serving as aspirational targets for manuscript submissions due to their visibility and citation potential.^[43] Libraries and universities incorporate impact factors into serials reviews to evaluate subscription viability, often prioritizing Q1 and Q2 journals for renewal while scrutinizing lower-quartile titles against budget constraints and usage metrics.^[44]^[45] In such processes, high-impact benchmarks guide decisions; CA: A Cancer Journal for Clinicians recorded an impact factor of 503.1 in 2024, exemplifying elite oncology journals retained for their influence despite costs.^[46] However, cancellations typically balance impact data with local circulation—low-impact journals with strong usage may persist, whereas underused high-impact ones face cuts during fiscal pressures, as seen in post-2008 economic analyses.^[47]^[48] Funders and institutions increasingly apply impact factor thresholds in steering open-access transitions, favoring high-quartile journals for transformative agreements that shift from subscriptions to article-processing charges.^[49] For researcher choices, these metrics inform submission strategies, with Q1 outlets prioritized for enhanced dissemination, though selectors caution against sole reliance on impact factors due to their aggregation biases across disciplines.^[50]

Academic evaluation and career advancement

In academic evaluations, journal impact factors (JIFs) are frequently employed as proxies for publication prestige, influencing hiring, promotion, and tenure decisions by signaling the perceived quality and reach of a researcher's work. Institutions often require candidates to include metrics such as the average JIF across their publications in tenure dossiers, treating it as a shorthand for scholarly productivity and influence.^[51] A 2019 analysis of review, promotion, and tenure (RPT) documents from 128 North American and international institutions revealed that JIFs were referenced in 40% of cases, predominantly to denote journal quality, impact, or prestige, with 87% of those institutions incorporating it into tenure assessments.^[51] This practice extends to hiring, where JIFs appear on CVs and in selection criteria to gauge a candidate's alignment with institutional research standards.^[52] National differences in JIF reliance are pronounced, with heavier emphasis in Asian systems compared to moderated approaches in Europe. In China, the National Natural Science Foundation of China (NSFC) has long prioritized publications in high-JIF, Science Citation Index (SCI)-indexed journals for grant evaluations and career progression, a policy rooted in reforms from the 1990s that tied funding and promotions to JIF thresholds as indicators of international competitiveness.^[53] This has incentivized researchers to target elevated JIF outlets for eligibility in NSFC competitions, where average JIF metrics contribute to scoring project proposals and principal investigator appraisals. In Europe, adoption of the San Francisco Declaration on Research Assessment (DORA) since 2012, endorsed by bodies like Science Europe in 2022, has prompted shifts toward qualitative peer review over JIFs in evaluations, reducing their weight in hiring and promotions at signatory universities while still acknowledging journal prestige contextually.^[54]^[55] Supporting JIF use in researcher assessments, empirical data from biomedical fields show moderate positive correlations between journal impact factors and independent quality evaluations. For example, a study of physician ratings for clinical journals found a strong association with JIFs (r² = 0.82, p = 0.001), suggesting alignment between citation-based metrics and expert judgments of relevance and rigor. In grant contexts, such as NSFC panels, higher average JIFs of prior outputs correlate with funding success rates, as they proxy for anticipated dissemination and peer validation, though direct causation remains mediated by field-specific norms.^[53] These correlations, typically ranging from 0.4 to 0.6 in peer-reviewed biomedical article assessments, underpin JIFs' role in prioritizing candidates with demonstrated publication trajectories in influential venues.^[56]

Funding, policy, and institutional decision-making

Performance-based research funding systems (PRFS), implemented in over a dozen countries including the United Kingdom, Australia, Norway, and Finland, allocate government research grants to universities and institutions based on evaluated outputs such as publications weighted by journal prestige, where impact factors serve as a key bibliometric proxy for quality.^[57] These systems typically assign points or scores to articles in high-impact factor journals to compute institutional performance metrics, influencing annual block funding distributions that can total billions in public expenditure; for instance, Australia's system directs approximately AUD 2 billion yearly through such evaluations.^[58] Despite methodological critiques, impact factors enable scalable comparisons across disciplines, shaping national priorities toward fields with journals exhibiting higher average citation rates.^[59] In China, institutional and policy incentives historically tied research funding and bonuses to journal impact factors to boost global competitiveness, with universities disbursing cash rewards scaled to impact factor thresholds—often exceeding $100,000 USD per paper in top-tier journals like Nature or Science—drawing from government-supported budgets.^[60] This approach, prevalent through the 2010s, correlated with a surge in Chinese-authored high-impact publications, from under 1% of global Nature/Science papers in 2000 to over 15% by 2016, though it prompted concerns over quality dilution and prompted a 2020-2023 policy shift prohibiting such monetary rewards in evaluations.^[61]^[62] University-level decision-making similarly leverages impact factors for internal resource allocation, such as seed grants or lab funding, prioritizing principal investigators with track records in high-factor journals to maximize institutional prestige and attract external sponsorship.^[51] Panels at agencies like the U.S. National Science Foundation and National Institutes of Health, while formally scoring grants on merit criteria excluding direct metrics, informally reference publication venues' impact factors to gauge project feasibility and investigator productivity during peer review.^[63] This practice persists amid declarations like DORA advocating restraint, as high-impact outputs signal potential for broader dissemination and follow-on funding.^[7]

Benefits

Standardization and comparability

The impact factor serves as a standardized metric calculated annually using a uniform formula across approximately 22,000 journals included in Clarivate's Journal Citation Reports, providing a consistent benchmark for evaluating journal influence irrespective of publication size or discipline.^[15] This approach applies the same citation-to-publication ratio—dividing the number of citations in a given year by the citable items published in the preceding two years—to all eligible titles, ensuring methodological consistency that minimizes variability in assessment criteria.^[64] By converting disparate citation patterns into a single numerical value, the impact factor diminishes subjectivity in journal evaluations, replacing ad hoc qualitative judgments with data-driven rankings that stakeholders can replicate and verify.^[65] This standardization enables direct cross-journal comparisons, such as ranking titles within or across categories based on relative citation efficiency, which supports informed resource allocation in research ecosystems.^[66] Annual releases of impact factors also permit temporal comparisons, allowing observation of trends like a journal's rising or declining citation reception over successive years, as evidenced by multi-year data series in citation databases.^[67] For example, libraries routinely incorporate impact factors into subscription renewal processes, canceling lower-impact titles to redirect funds toward higher-cited journals amid fiscal pressures, thereby aligning collections with demonstrated scholarly usage.^[44] This linkage of journal standing to empirically observable citation flows enforces accountability, as prestige accrues from verifiable patterns of scholarly engagement rather than unquantified reputation alone.^[68]

Empirical correlations with research quality

Empirical analyses have identified moderate positive correlations between journal impact factors (IFs) and indicators of research quality, including peer-assessed journal prestige and article-level citation rates. A 2017 systematic review and meta-analysis of studies comparing IFs to perception-based rankings by experts reported a pooled correlation coefficient indicating a moderate positive relationship, suggesting that higher IFs align with scholarly evaluations of journal quality across disciplines.^[69] In clinical neurology, for instance, the correlation between IFs and mean peer ratings of journals reached 0.67 (p < 0.01), reflecting consistent alignment within that field.^[70] Higher IFs also associate with enhanced peer review processes that contribute to output quality. An analysis of 10,000 peer review reports from 1,644 biomedical journals found that reviews for higher-IF journals (up to 74.7) contained a greater proportion of comments on study methods (51.8% in top-IF group versus 40.4% in lowest), indicating more thorough scrutiny of methodological rigor compared to lower-IF outlets.^[56] This selective filtering likely elevates average article validity, as evidenced by meta-epidemiological data from 2,459 study results across 446 Cochrane reviews, where higher IFs predicted results closer to pooled "truth" estimates, with relative deviation decreasing by 0.023 per IF unit (95% CI: -0.32 to -0.21; p < 0.001).^[71] Regarding citation-based influence, articles published in higher-IF journals systematically garner more citations, even after accounting for self-selection, due to both pre-publication selection of impactful work and post-publication prestige effects. Causal inference studies confirm that placement in high-IF journals augments citations beyond what the article's inherent qualities alone would attract, with within-field analyses preserving IF as a predictor of relative performance.^[72] These patterns hold despite field variations, underscoring IFs' utility in signaling prospective scientific influence and validity.^[73]

Incentives for scientific rigor and dissemination

High-impact journals, motivated by the need to sustain elevated impact factors, implement more stringent peer review processes that emphasize methodological scrutiny. A 2023 analysis of over 1,000 peer review reports from biomedical journals found a positive correlation between journal impact factor and review thoroughness, with higher-impact outlets providing more detailed feedback on study methods (e.g., an average of 1.5 additional methodological comments per report compared to lower-impact journals) while de-emphasizing novelty.^[56] This selective pressure elevates overall publication standards by incentivizing authors to produce robust, error-minimized work to meet acceptance thresholds. Empirical data indicate that this rigor translates to fewer retractions attributable to errors in high-impact venues. A comprehensive review of over 2,000 retracted publications revealed a strong negative correlation (r = -0.91, p < 0.0001) between journal impact factor and retraction rates for honest errors, such as analytical mistakes, contrasting with no such pattern for misconduct-driven retractions.^[74] By prioritizing verifiable quality over volume, impact factors thus discourage the submission and acceptance of flawed research, fostering a publication ecosystem where only high-utility findings advance. Impact factors also promote broader dissemination practices, as journals adapt policies to maximize citations and visibility. For instance, PLOS ONE's impact factor surged from its inaugural 4.4 in 2010 to a peak of 4.0 by 2013, driven by its open-access model that expanded readership and citation potential across disciplines, encouraging inclusive criteria for sound science over perceived prestige.^[75] Similarly, mandates for open data in high-impact outlets correlate with citation uplifts; studies show articles with publicly shared datasets receive up to 69% more citations, independent of journal prestige, prompting editors to incentivize such transparency to bolster their metrics.^[76] At a systemic level, impact factors allocate scarce academic attention toward meritorious outputs, enabling subsequent research to build on validated, high-diffusion knowledge. This merit-aligned mechanism ensures that resources—such as funding and collaboration opportunities—flow preferentially to impactful work, reinforcing a cycle of rigorous inquiry and knowledge accumulation without relying on subjective assessments.^[77]

Criticisms

Field-specific and scale biases

The impact factor's reliance on arithmetic means renders it particularly susceptible to skewness in citation distributions, which are empirically observed to follow highly right-skewed, log-normal, or power-law patterns across journals and fields. In such distributions, a small fraction of highly cited articles disproportionately influences the mean, with studies showing that approximately 15-20% of papers often account for 50% or more of a journal's citations, thereby inflating the impact factor beyond the typical article's performance.^[78]^[79] This skewness undermines the metric's representativeness, as medians or other central tendency measures would yield substantially lower values, highlighting a scale bias where outliers distort journal-level assessments. Field-specific citation norms exacerbate these scale issues, with disciplines exhibiting divergent average citation rates and temporal dynamics that preclude valid cross-field comparisons using raw impact factors. For instance, biomedical and biological sciences journals typically exhibit higher average impact factors (often 5-10 or more for leading titles), driven by rapid publication cycles and frequent referencing practices, whereas mathematics and physics journals average below 2, reflecting sparser and more deliberate citation habits.^[80]^[66] These disparities arise from inherent differences in reference densities—e.g., biology papers averaging 30-50 citations versus 10-20 in economics or mathematics—rather than quality variances, rendering normalized or field-adjusted metrics necessary for equitable evaluation.^[81] In humanities and social sciences, the impact factor's applicability is further limited by slower citation accumulation cycles, lower overall rates, and a reliance on non-journal formats like monographs, which fall outside Web of Science indexing. Top humanities journals, when indexed, rarely exceed impact factors of 1-2 due to regionally focused scholarship and delayed peer influence, contrasting sharply with sciences' global, fast-paced citation environments.^[82]^[83] Consequently, applying unadjusted impact factors across fields distorts prestige rankings and resource allocation, as evidenced by systematic underrepresentation of humanities in high-impact factor databases.^[84]

Manipulation and editorial distortions

Journals have engaged in excessive self-citation practices to artificially elevate their impact factors, where authors affiliated with the journal disproportionately cite its prior publications, inflating the citation numerator. Clarivate Analytics suppresses impact factors for journals exhibiting self-citation rates that distort performance metrics, such as when self-cites exceed 20-30% of total citations, far above the norm for high-quality journals. In the 2018 Journal Citation Reports, 14 journals were denied impact factors due to elevated self-citation levels, demonstrating coordinated efforts among editorial boards and authors to boost rankings. Similarly, in 2025, 20 journals lost their impact factors for excessive self-citation or citation stacking, where clusters of papers mutually cite one another to amplify counts.^[85]^[29]^[86]^[87] Publishers strategically favor review articles over original research to manipulate impact factors, as reviews garner higher citation rates—often 2-5 times more than primary articles—while contributing equally to the denominator of citable items. This selective emphasis increases the numerator without proportionally expanding the publication base, a tactic observed in fields like biomedicine where journals shifted toward review-heavy issues in the early 2000s to climb rankings. Such distortions undermine the metric's validity, as the inflated factor reflects editorial curation rather than broad scholarly influence.^[88] Coerced citations and citation farms further enable manipulation, with editors pressuring submitting authors to include superfluous references to the journal's past works, a practice deemed inappropriate by 85% of surveyed researchers yet complied with by nearly 60% to secure publication. Citation farms involve organized groups where a small set of authors generate disproportionate citations—sometimes over 50% from mutual referencing—to prop up impact factors, evident in audits revealing 4.2% of science journals in 2010 deriving more than half their impact factor numerator from self-cites. These tactics, documented in business and health sciences journals, highlight systemic incentives for editorial interference over merit-based evaluation.^[89]^[90]^[91]

Misapplication to individuals and systemic harms

The journal impact factor (JIF), as a mean value, cannot reliably proxy the citation performance of individual articles due to the highly skewed distribution of citations within journals, where a small fraction of papers typically garners the majority of citations.^[92] For instance, analyses of citation data across disciplines show that the top 10% of papers often account for 50% or more of a journal's total citations, following power-law or log-normal distributions that render averages misleading for assessing any single publication.^[93] This skewness implies that many articles in high-JIF journals receive few or no citations, undermining the fallacy of attributing journal-level prestige to personal outputs without direct evaluation of content or impact.^[94] Extrapolating JIF to individuals perpetuates errors in hiring, promotion, and tenure decisions, where researchers are judged by the venues of their publications rather than the substance or verifiable influence of their work.^[95] Scientometric experts consistently argue against this practice, noting that it conflates aggregate journal metrics with personal merit, leading to undervaluation of substantive contributions in lower-JIF outlets or overvaluation of marginal papers in elite ones.^[96] Such misapplication distorts career trajectories, favoring quantity and venue prestige over innovation or reliability, as evidenced by institutional reliance on JIF thresholds despite their disconnect from article-level quality.^[97] At a systemic level, the "publish or perish" imperative amplified by JIF chasing incentivizes practices like p-hacking—manipulating data for statistical significance—and salami slicing, where single studies are fragmented into minimal publishable units to inflate output counts for high-JIF submissions.^[98] Surveys of biomedical researchers reveal that nearly 75% perceive a reproducibility crisis, attributing it to pressures prioritizing novel, positive results in prestigious journals over robust, replicable methods, which discourages preregistration or null findings.^[99] This culture fosters suppression of negative results, as high-JIF outlets selectively publish confirmatory outcomes, exacerbating the replication crisis through selective reporting and reduced scientific self-correction.^[100] Consequently, resources and talent shift toward gaming metrics rather than advancing knowledge, eroding trust in published findings across fields.^[101]

Responses and Reforms

Academic declarations and guidelines

The San Francisco Declaration on Research Assessment (DORA), initiated in 2012 at the American Society for Cell Biology meeting, calls for evaluating research based on its intrinsic qualities rather than journal-level metrics like the impact factor.^[102] It explicitly recommends against using journal impact factors as surrogates for the quality of individual articles or scientists' contributions in hiring, promotion, or funding decisions, advocating instead for assessments that consider the content of publications, peer review, and broader societal impacts.^[103] As of 2025, DORA has been signed by over 26,000 individuals and organizations across 168 countries, including research funders, institutions, and publishers.^[104] Complementing DORA, the Leiden Manifesto, published in 2015, outlines ten principles for the responsible use of quantitative metrics in research evaluation.^[105] These principles emphasize that metrics should support, not replace, qualitative expert judgment; be contextualized against specific institutional missions and disciplines; and avoid simplistic rankings that ignore variability in citation practices across fields. Key tenets include keeping data collection and analysis transparent to allow scrutiny, recognizing the distinct contributions of scholars beyond citations (such as mentoring or policy influence), and ensuring metrics align with diverse research goals rather than prioritizing publication counts or journal prestige.^[106] Despite these declarations, empirical evidence indicates limited systemic adoption, with journal impact factors remaining prevalent in academic evaluations. A 2019 analysis of review, promotion, and tenure documents from 99 institutions worldwide found the Journal Impact Factor referenced in 81% of cases, often as a proxy for research quality.^[107] Similarly, studies post-DORA show persistent correlations between journal rankings and expert assessments, suggesting entrenched reliance on such metrics endures amid institutional inertia and lack of enforceable alternatives.^[108] This ongoing use has prompted calls for stronger implementation mechanisms, though declarations alone have not curtailed impact factor dominance in funding and career decisions.^[51]

Publisher and database adaptations

Clarivate, administrator of the Journal Citation Reports (JCR), introduced the Journal Citation Indicator in May 2021 as a field-normalized complement to the traditional Journal Impact Factor, calculating average citation impact relative to a global baseline where a score of 1.0 denotes performance at the field average across Web of Science categories.^[109] This adaptation addresses scale and discipline biases by enabling cross-field comparisons without raw citation volume disparities.^[110] To curb self-citation excesses, Clarivate maintains thresholds triggering journal reviews and potential suppression from JCR; for example, in the 2025 release, 20 journals lost Impact Factors due to elevated self-citation rates exceeding 25% or citation stacking patterns.^[87] Beginning with the 2025 JCR (incorporating 2024 data), citations to or from retracted articles are fully excluded from the Impact Factor numerator, preventing artificial inflation from invalid references and reinforcing metric integrity against post-publication retractions.^[34] Publishers have responded with policy enhancements promoting transparency in practices that could affect Impact Factors. Nature Portfolio, for instance, expanded transparent peer review to all research articles by June 2025, publishing reviewer comments and author responses alongside accepted papers to foster accountability and reduce selective incentives tied to citation metrics.^[111] Springer Nature's editorial policies similarly prioritize independence from Impact Factor pressures, mandating disclosure of data, materials, and code to support reproducible research over metric optimization.^[112] These steps, alongside Clarivate's exclusions, have sustained the metric's use by systematically flagging and mitigating distortions, as reflected in persistent but targeted JCR suppressions.^[13]

Policy shifts in evaluation practices

Numerous academic institutions and funding agencies have revised evaluation criteria to curtail the prominence of journal impact factors (IFs), favoring multifaceted reviews that prioritize research content, investigator expertise, and societal relevance over proxy metrics. This trend accelerated following widespread adoption of the San Francisco Declaration on Research Assessment (DORA) in 2012, which explicitly advises against employing journal-based metrics like IFs as surrogates for individual article or researcher quality. By 2025, over 2,500 organizations worldwide, including universities, funders, and publishers, had signed DORA, committing to policy reforms that integrate qualitative peer assessments and diverse indicators.^[7] Prominent funding bodies exemplify these changes. The European Research Council (ERC), a major European Union grant provider, announced in July 2021 its decision to eliminate IFs and analogous metrics from principal investigator evaluations, shifting emphasis to the intrinsic merit of proposals via expert panels assessing scientific excellence, feasibility, and innovation.^[113] Similarly, the U.S. National Institutes of Health (NIH) has maintained review frameworks since the early 2000s that exclude bibliometric scores like IFs, focusing instead on five core criteria: significance, investigator qualifications, innovation, approach, and environment; in the 2020s, NIH implemented simplified peer review for applications due from January 2025 onward, further streamlining to highlight rigorous, high-potential science without metric quantification. At the institutional level, universities have enacted parallel prohibitions. Utrecht University in the Netherlands discontinued IF usage in hiring, promotion, and tenure decisions around 2021, substituting it with evaluations of open access contributions and qualitative impact reviews.^[114] Other DORA signatories, such as those in the UK and Canada, have integrated similar guidelines into faculty handbooks, mandating narrative assessments over numerical thresholds.^[115] Outcomes of these reforms show variability. Policies have demonstrably curbed overt IF gaming in compliant settings by decoupling career advancement from journal prestige, yet empirical analyses indicate persistent reliance in competitive fields like biomedicine, where informal metric considerations endure despite formal bans, potentially slowing holistic adoption.^[116] Longitudinal studies remain limited, but early indicators suggest enhanced focus on reproducible research amid reduced publication volume pressures in reformed institutions.

Alternatives

Other journal-level metrics

Several journal-level metrics have emerged as complements or alternatives to the Journal Impact Factor (JIF), aiming to mitigate its vulnerabilities such as susceptibility to citation outliers, lack of prestige weighting, and insufficient normalization for field-specific citation practices. These metrics, derived primarily from large databases like Scopus, incorporate iterative algorithms or normalization techniques to provide a more nuanced assessment of a journal's influence within its scholarly network.^[117]^[118] The SCImago Journal Rank (SJR) quantifies the prestige of a journal by modeling citations as transfers of scientific influence, akin to an eigenvector centrality measure similar to PageRank. Developed using Scopus data, SJR calculates a journal's score iteratively: each citation's value is weighted by the prestige of the citing journal, with the process repeated until convergence, typically over three years of citations to prior publications. This approach addresses JIF's equal treatment of all citations by diminishing the influence of self-citations and elevating those from high-prestige sources, thereby better capturing a journal's standing in the global citation network rather than raw volume. SJR values are dimensionless, with higher scores indicating greater relative prestige; for instance, journals like Nature consistently rank high due to citations from influential peers.^[117]^[119] The Source Normalized Impact per Paper (SNIP) provides field-normalized citation impact by dividing a journal's raw citation rate by the aggregate citation potential of its subject category. Computed from Scopus, SNIP uses citations in the current year to publications from the prior three years, normalized against the median citation rate in the field (termed "rip" or relative impact potential), which accounts for varying publication volumes and citation densities across disciplines. Unlike JIF, which favors fields with naturally higher citation rates like biomedicine, SNIP penalizes less by rewarding contextual performance and inherently downweights uncited papers through its per-paper averaging. This makes SNIP particularly useful for cross-disciplinary comparisons, with values above 1 indicating above-average impact in the field; it was introduced by the Centre for Science and Technology Studies (CWTS) at Leiden University in 2010 to promote fairer evaluations.^[118]^[120] The h-index adapted for journals measures the balance between a journal's productivity and its citation endurance, defined as the largest number h such that at least h articles published in the journal have each received at least h citations. Typically calculated over all-time citations using databases like Google Scholar or Scopus, the journal h-index resists distortion from a handful of highly cited outliers that inflate JIF means, instead emphasizing consistent impact across a core set of papers. For example, a journal with h=50 has 50 papers cited at least 50 times each, providing a robust indicator less prone to manipulation via selective citing practices. Originally proposed by Jorge Hirsch in 2005 for authors, its journal application gained traction for evaluations where median-like robustness is preferred over arithmetic averages, though it accumulates over time and may undervalue newer journals.^[121]^[122]

Article-level and author-centric measures

Article-level metrics evaluate the influence of individual publications by normalizing citations against field-specific expectations, circumventing the aggregation inherent in journal impact factors. Author-centric metrics aggregate these assessments to gauge a researcher's sustained contributions, emphasizing productivity alongside citation thresholds rather than recency-biased averages. These approaches prioritize direct evidence of impact on subsequent work, though they remain susceptible to citation practices like self-citation or field citation rate variations.^[123] The Relative Citation Ratio (RCR), introduced by the U.S. National Institutes of Health (NIH) in 2016, computes an article's scientific influence as the ratio of its actual citations per year to the expected citations for publications in the same field, year, and type.^[123] For instance, an RCR exceeding 1 signifies above-average impact relative to NIH-funded peers, with percentiles enabling cross-field comparisons; a median RCR across NIH grants was approximately 0.99 in analyses of over 900,000 publications from 2009–2013.^[123] Unlike raw citation counts, RCR adjusts for publication age and discipline-specific norms, such as lower rates in mathematics versus biomedicine, but requires comprehensive databases like PubMed for accuracy.^[123] It has been integrated into NIH's iCite tool for portfolio analysis, revealing that R01-funded papers average RCRs around 1.1.^[123] For authors, the h-index, proposed by physicist Jorge E. Hirsch in a 2005 Proceedings of the National Academy of Sciences paper, defines h as the maximum value where a researcher has h papers each receiving at least h citations.^[124] This metric resists inflation from a few highly cited outliers, correlating empirically with career recognition like Nobel Prizes in physics, where laureates averaged h around 40–50 by award year.^[124] However, it disadvantages early-career researchers due to accumulation time and fields with sparse citations, prompting variants like time-normalized h.^[124] The g-index extends the h-index by identifying the largest g such that the researcher's top g articles collectively amass at least g² citations, amplifying credit for highly influential works while incorporating lower-cited ones marginally.^[125] Developed by Leo Egghe in 2006, it yields higher values than h—for example, an h of 20 might correspond to a g of 30 if citations skew heavily—better capturing uneven impact distributions observed in empirical studies of publication sets.^[126] Like the h-index, g-index values rise with career length but offer a more nuanced view of "global citation performance."^[125] Altmetrics track alternative indicators of engagement, including social media shares (e.g., Twitter mentions), downloads, and policy citations, aggregated into scores like the Altmetric Attention Score, which weights sources such as news outlets (factor of 1) over Wikipedia (0.5).^[127] Introduced around 2011, these metrics reveal rapid dissemination—for a sample of biomedical articles, Twitter mentions correlated modestly (r=0.2–0.3) with eventual citations but highlighted public interest absent in citation data.^[127] They complement citations by evidencing societal reach, as in cases where datasets garner GitHub forks or blogs spark discussions, yet risks include gaming via bots or conflating visibility with validity, with scores varying widely by discipline publicity.^[127]

Emerging multidimensional assessments

The CWTS Leiden Ranking employs field-normalized bibliometric indicators to evaluate university-level scientific performance, incorporating metrics such as the mean normalized citation impact (MNCS), which adjusts for differences in citation practices across disciplines, alongside publication counts and collaboration breadth, to provide a balanced view of research output and influence.^[128] This approach counters biases inherent in unnormalized citation counts by benchmarking publications against global field averages, enabling comparisons of institutional impact without favoring high-citation fields like biomedicine over others.^[129] The 2024 edition covers over 1,500 universities, emphasizing normalized scores to reflect causal contributions to knowledge advancement rather than raw volume.^[130] Dimensions badges integrate citation data with altmetrics to visualize multifaceted article-level impact, displaying not only citation tallies but also online mentions, policy citations, and media attention, which capture societal and practical reach beyond academic echo chambers.^[131] Introduced in 2018 and refined thereafter, these badges embed in publisher platforms to offer immediate, interactive overviews, blending quantitative scholarly metrics with qualitative diffusion indicators for a holistic snapshot.^[132] ORCID integrations further enable this by linking persistent researcher identifiers to diverse data sources, facilitating aggregated profiles that combine bibliometric scores, funding records, and collaboration networks for author-centric evaluations. Such linkages, as implemented in systems like Stanford's centralized identity management since 2024, support comprehensive assessments by verifying affiliations and outputs, reducing duplication errors in multidimensional scoring.^[133] Emerging frameworks extend these integrations toward predictive and contextual depth; for instance, the MARIA model assesses research impact across responsiveness to needs, accessibility of outputs, reflexivity in methods, intentionality in dissemination, and accountability in ethics, applied in self-evaluations to incorporate sustainability and stakeholder effects.^[134] Similarly, the 2025 AACSB Global Research Impact Framework advocates principles for evaluating business scholarship through scholarly, educational, societal, and economic dimensions, prioritizing qualitative evidence and stakeholder testimonials over isolated metrics to align with real-world causal chains.^[135] These tools address the impact factor's temporal myopia by forecasting long-term trajectories via normalized baselines and AI-assisted pattern recognition in citation trajectories, though empirical validation remains nascent.^[136]

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Methodology - CWTS Journal Indicators
SNIP. The source normalized impact per publication, calculated as the number of citations given in the present year to publications in the past three years ...
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SJR - Help
Journal title. SJR (SCImago Journal Rank): trend graph of this metric and the value obtained in the last year available. Journal title. Best Quartile and ...
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Measuring a journal's impact - Elsevier
Source Normalized Impact per Paper (SNIP) is a sophisticated metric that intrinsically accounts for field-specific differences in citation practices. It does ...
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h-index for journals - Research Metrics
Sep 11, 2025 · The h-index can be calculated for journals as well as authors. The value h is the largest number where at least h articles in that publication were cited at ...
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What is a good H-index? | Elsevier Author Services Blog
Basically, the H-index score is a standard scholarly metric. Here you can find what is considered a Good H-index and how to get your work to that point.
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Relative Citation Ratio (RCR): A New Metric That Uses ... - NIH
We use the RCR metric here to determine the extent to which National Institutes of Health (NIH) awardees maintain high or low levels of influence on their ...
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An index to quantify an individual's scientific research output - PNAS
I propose the index h, defined as the number of papers with citation number ≥h, as a useful index to characterize the scientific output of a researcher.
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Full article: Understanding the 'g-index' and the 'e-index'
May 6, 2021 · The g-index is defined as 'the largest number such that the top 'g' articles received together at least g2 citations. ... The citations are ...
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Metrics: h and g-index - Harzing.com
Feb 6, 2016 · The g-index is calculated based on the distribution of citations received by a given researcher's publications, such that: given a set of ...
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The Altmetric Attention Score: What Does It Mean and Why Should I ...
The Altmetric score represents a weighted count of the amount of attention for a research output from a variety of sources.
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CWTS Leiden Ranking
The CWTS Leiden Ranking 2024 provides insights into the scientific performance of over 1500 universities using advanced bibliometric indicators, with list, ...Ranking 2018 · Ranking 2017 · Ranking · Ranking 2023Missing: normalization | Show results with:normalization
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[PDF] Bibliometrics for Research Management and Research Evaluation
The. Leiden Ranking is produced annually by CWTS based on data from Web of Science. It offers bibliometric indicators of scientific output, impact, and ...
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Information - Indicators - CWTS Leiden Ranking
The CWTS Leiden Ranking 2024 offers a sophisticated set of bibliometric indicators that provide statistics at the level of universities on scientific impact.Missing: multidimensional | Show results with:multidimensional
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Showcase your impact with Dimensions Badges
Mar 22, 2018 · The Dimensions Badges give an immediate insight into the impact an article has had through citation data and altmetrics.Missing: multidimensional | Show results with:multidimensional
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Dimensions Badges: A new way to see citations - Altmetric
Jan 26, 2018 · Dimensions badges are interactive visualizations that showcase the citation data for individual publications.
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Case Study: Stanford University Integrates ORCID into a Centralized ...
Aug 6, 2024 · Implementing ORCID at this level can be a more streamlined approach than managing multiple ORCID integrations into many local systems.
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MARIA evaluation model: multidimensional method for self ...
The MARIA method is a light-touch evaluation tool that looks at research impact from multiple dimensions and considers its ethics and sustainability.1/ Responsiveness · 2/ Accessibility · 3/ Reflexivity
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https://www.aacsb.edu/insights/reports/2025/research-impact-framework-exposure-draft
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(PDF) ARTICLE Multidimensional impact of research: developing ...
Oct 31, 2023 · This article introduces the impact assessment process of 23 research projects of the Capes prInt Program aimed at internationalizing Brazilian ...