Artificial Intelligence (AI) and Machine Learning (ML) technologies are rapidly transforming scholarly publishing, with far-reaching implications for English Studies. AI tools now assist at every stage of research-from literature discovery and writing to citation analysis and editorial selection. For example, AI-powered assistants like Elicit and Scholarcy streamline literature reviews by summarizing large volumes of texts, while grammar and editing tools (e.g. Grammarly, Wordvice AI) help non-native English researchers refine their writing. Publishers and platforms similarly harness AI: Elsevier's Journal Finder matches manuscripts to "over 2,500 peer-reviewed" journals, and bibliometric tools like scite.ai classify citation contexts automatically. Large-scale text-mining methods (topic modeling, stylometry, distant reading) enable English scholars to analyze entire literary corpora and even predict emerging trends in fields such as eco-criticism or digital humanities. At the same time, AI raises serious ethical questions. Committee on Publication Ethics (COPE) guidelines emphasize that AI cannot be listed as an author, since AI "has no legal standing" to take responsibility, and authors must disclose AI use and remain accountable for all content. Critics also warn that AI tends to produce homogeneous output. Conversely, AI offers opportunities: neural translation and editing tools promise a future where "everyone can use their own language to write, assess and read science". In sum, English Studies stands at a crossroads. If adopted judiciously, AI can democratize research and enable novel digital humanities methods, but unchecked reliance risks eroding creativity, interpretative plurality, and scholarly integrity. This paper surveys existing literature and emerging practice to chart AI's benefits and pitfalls for the future of publishing in English Studies.

To assess the ongoing citation of the 2009 Nature Genetics article by Ross et al linking TPMT to cisplatin-induced ototoxicity and to evaluate the extent to which its disputed findings persist in the literature. A total of 378 Google Scholar citations of the 2009 Nature Genetics publication from 2009 to 2025 were examined, of which 214 PubMed-indexed manuscripts were screened and categorized by citation nuance (positive, negative/mixed) and citation characteristics using the framework proposed by Suelzer et al in a 2019 JAMA Network Opens study. Citation context was independently assessed by two reviewers, with discrepancies resolved by consensus of all three authors. Most articles cited the 2009 publication in a favorable or uncritical manner, even in publications after 2015 US Food and Drug Administration's cisplatin label revision acknowledging that the 2009 findings were flawed. A minority of citations acknowledged conflicting evidence or questioned the study's validity. Although the 2009 publication's findings have been widely challenged, many subsequent authors continue to cite the study without acknowledging its limitations. This pattern highlights the need for stronger postpublication oversight, more transparent editorial practices by high-impact journals, and greater critical engagement by authors when citing foundational literature to ensure that clinical and research narratives are shaped by reliable and current evidence.

This paper examines the evolution of academic citation practices from print into digital contexts. While standardised citation styles only became widespread in the mid-twentieth century, the digital era has raised new questions about credibility, authority, and the recognition of diverse sources. Contemporary shifts—including the rise of alternative scholarly outputs, decolonisation movements, and artificial intelligence—have highlighted various forms of citational injustice and misbehaviours, prompting corrective interventions. While these approaches represent progress, they often remain narrowly focused and lack a broader theoretical orientation capable of driving multi-level systemic change in the context of developing technologies such as artificial intelligence, which are being used by some scholars as research tools. This paper serves as a foundation for developing a new citational justice framework in future research. Working towards improved citational justice is not only about recognising, acknowledging, and combating citational and epistemic injustice but also about rethinking the academic culture of research communications in digital citation contexts. Collective action and sustained structural transformation are needed to reshape digital citation practices towards more equitable and transparent paradigms of knowledge production in the rapidly changing digital publishing landscape.

BackgroundSince 2010, the UK’s National Institute for Health and Care Research has funded a policy research unit (PRU) focused on maternal and neonatal health, with a remit to build an evidence base for policy and clinical practice in this field. We explored the usefulness of the platforms Overton and Altmetric as tools to gain insight into the use of PRU research evidence in policy and practice.MethodsWe searched Overton and Altmetric using article DOIs to identify citations of PRU-funded articles in policy documents and clinical guidelines. We excluded citations of the research in lists of excluded evidence, academic journal articles, and unverifiable citations. To obtain a count of unique citing documents for each article, we merged multiple editions/versions, translations, and duplicates of the same document. We calculated latency from article publication date to citation date, and citation distribution over time. We also developed descriptive case studies to explore how the citing policy documents used highly-cited research evidence.ResultsThe 110 published articles reporting research funded by the PRU received 134 unique policy document and clinical guideline citations; 43/110 articles (39%) were cited in at least one document. Most citing documents were authored by organisations based in the UK (52/134) and other high-income countries. Intergovernmental organisations accounted for around 15% of citations (20/134). The median time from article publication to citation was 183 weeks (range 0.4–575 weeks). Citation contexts varied; use of the evidence in citing documents included provision of general background information, detailed summaries of findings, and support/rationale for specific clinical recommendations.ConclusionsOverton and Altmetric are useful tools for identifying and exploring the use of research evidence in healthcare policy and clinical guidance. However, citation analysis alone cannot provide the complete picture. The delay between evidence publication and use in policy warrants further investigation.

ABSTRACT The diversity and interdisciplinarity of kinesiology contribute to visibility and subject classification inconsistency of the field in databases. This study reports citation metrics and subject areas for kinesiology-related journals based on Cabells and other databases. Eighty-six journals were identified based on multiple searches of Cabells and samples of discipline-matched and interdisciplinary journals for comparison. Major disciplinary and research topics were identified using Cabells Classification Index (CCI©). Kinesiology-related journals were rigorously reviewed with a median acceptance rate of 26% and unweighted scite index (0.871) similar to the comparison groups. The CCI© classified kinesiology-related journals into numerous general disciplines (10 of 18) and 23 specific research topic areas. The scite index was not meaningfully correlated with four common journal metrics, which were moderately to strongly correlated with each other. Cabells offers greater coverage of psychology, physical education, and sports management journals and unique positive citation context information with their scite index.

Abstract Although Howard R. Bowen’s 1953 book Social Responsibilities of the Businessman (hereafter SRB ) is regarded as a classic in the fields of business ethics and management, the impact of SRB has been taken for granted and has rarely been evaluated. Problematizing the use of classics as core to the advancement of scholarly fields, we analyze the purpose underlying the citation of SRB ( why it is cited) as well as the breadth ( what is cited) and the depth ( how much is being discussed) of scholarly engagement with this classic. Combining analytical induction, quantification and a critical citation context analysis of 435 citations from 320 articles citing SRB between 1953 and 2023, we identified three main categories of use— arguing , defining , and landmarking —and evaluated the depth and breadth of such scholarly uses of SRB. Our results reveal how these uses differ across disciplinary domains and over time. Our analysis of breadth and depth confirms the continuous superficial engagement with SRB . We rely on this case to conceptualize the paradoxical legacy of Bowen, discuss ethical issues inherent to the politics of citation and evaluate what ‘fair’ uses of classics could be.

A systematic review was conducted to provide an overview of the performance of optically tracked instrumented pointers in motion analysis. Due to the insights provided, relevant research on pointers using very similar principles in surgical navigation is also included. English language articles published up until April of 2025 were searched with keywords like 'pointer', 'anatomical landmark' and 'motion analysis' that report on a measurement design adequate to assess pointer performance in appropriate detail. The included databases were Scopus, ScienceDirect, PubMed and Web of Science. The search resulted in a total of 192 unique articles. Two researchers screened the records and followed up on references that were potentially relevant based on their citation context. One hundred twenty-five additional articles were identified this way. A total of 24 papers were included in the final review: 16 related to surgical navigation, and 8 to motion analysis. Based on the narrative synthesis, motion capture-based pointers for motion analysis can have errors as large as 5mm, while the expected errors of surgical navigation pointers are below 1mm. The most critical factors in pointer performance, besides the measurement volume of the tracking system, are the instrument's length, the arrangement of the tracked instrumenting markers and the quality of the tip's calibration, which determines the relative position of the pointer tip compared to the instrumenting markers. The insights from the research covered in this review can help set a benchmark for performance for current and future localiser systems, both in motion analysis and in surgical navigation. Users of such systems can use this information to check and compare the performance of their systems, while developers and researchers can use the data to develop more accurate systems and pointers.

ABSTRACT Increasingly, reviewers are asked to engage with citation‐based indicators, yet we know little about how those numbers are actually translated into judgements. This article builds on an extensive literature review in which the author identifies “chains of reasoning” that connect metrics with factors such as quality, prestige, recognition, and career potential. The study analyses 174 negative habilitation review reports (2012–2021) from four disciplines in Poland—Art History, Sociology, Mathematics, and Materials Engineering—to map how reviewers reason with metrics in practice. We identify and code passages where quantitative indicators are transformed into qualitative claims and reconstruct the “chains of reasoning” that link numbers to evaluative conclusions. The prevalence of metric‐based reasoning varies sharply by field—from about 4% of reviews in Art History to 71% in Materials Engineering—while Mathematics and Sociology occupy intermediate positions. Across disciplines, metrics are interpreted primarily as signals of recognition or visibility and influence or uptake; direct inferences from metrics to intrinsic quality are comparatively rare. Reviewers sometimes inspect citation contexts (e.g., self‐citations) and time lags, but explicit reflexivity about the limitations of indicators is infrequent. Crucially, metrics never stand alone: even the most metric‐intensive reviews combine numbers with content‐based critique. We catalogue nine recurring reasoning types and show how a uniform policy that mandates attention to metrics yields heterogeneous, discipline‐specific evaluative practices. The article advances scholarship on responsible research assessment by explaining how metrics travel from numbers to judgements and by offering practical guidance: use indicators as contextual evidence, make interpretive assumptions explicit, and calibrate benchmarks to disciplinary evaluative cultures.

Research evaluation metrics comparison: does citation or citation context count differ from citation context similarity metric?

ABSTRACTThis paper conducts a systematic, data‐driven analysis of H. Igor Ansoff's enduring impact on strategic management scholarship. Despite Ansoff's recognition as the “father of strategic management” and the continued relevance of his frameworks—including the Ansoff matrix, weak signals analysis, and strategic thrust—a comprehensive understanding of his influence remains incomplete. While previous research has documented the adoption of his concepts, few studies have empirically examined how his theories have influenced subsequent literature or been critiqued over time. We address these shortcomings through topic modeling, citation context analysis, and historiography applied to Web of Science publications citing Ansoff's Corporate Strategy (1965–2024). Our methodology identifies which concepts are most frequently referenced, whether citations are substantial or peripheral, and how the literature developed over time. This empirical approach reveals thematic developments in Ansoff‐related research, highlights underexplored theoretical elements, and demonstrates how his frameworks continue to shape contemporary strategic thinking. The findings contribute to understanding how classical strategic management theories maintain relevance in today's business landscape.

The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and lack mechanisms to incorporate full-text content or time-awareness. This review systematically examines the landscape of scholarly publication recommender systems, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology for a comprehensive and transparent selection of relevant studies. We highlight the limitations of current systems and explore the potential of integrating fine-grained citation knowledge—such as citation proximity, context, section, graph, and intention—extracted from full-text documents. These elements have shown promise in enhancing both the contextual relevance and recency of recommendations. Our findings highlight the importance of moving beyond accuracy-focused metrics toward user-centric evaluations that emphasise novelty, diversity, and serendipity. This paper advocates for the development of more holistic and adaptive recommender systems that better align with the evolving needs of researchers.

Since 2010, the UK's National Institute for Health and Care Research has funded a policy research unit (PRU) focused on maternal and neonatal health, with a remit to build an evidence base for policy and clinical practice in this field. We explored the usefulness of the platforms Overton and Altmetric as tools to gain insight into the use of PRU research evidence in policy and practice. We searched Overton and Altmetric using article DOIs to identify citations of PRU-funded articles in policy documents and clinical guidelines. We excluded citations of the research in lists of excluded evidence, academic journal articles, and unverifiable citations. To obtain a count of unique citing documents for each article, we merged multiple editions/versions, translations, and duplicates of the same document. We calculated latency from article publication date to citation date, and citation distribution over time. We also developed descriptive case studies to explore how the citing policy documents used highly-cited research evidence. The 110 published articles reporting research funded by the PRU received 134 unique policy document and clinical guideline citations; 43/110 articles (39%) were cited in at least one document. Most citing documents were authored by organisations based in the UK (52/134) and other high-income countries. Intergovernmental organisations accounted for around 15% of citations (20/134). The median time from article publication to citation was 183 weeks (range 0.4-575 weeks). Citation contexts varied; use of the evidence in citing documents included provision of general background information, detailed summaries of findings, and support/rationale for specific clinical recommendations. Overton and Altmetric are useful tools for identifying and exploring the use of research evidence in healthcare policy and clinical guidance. However, citation analysis alone cannot provide the complete picture. The delay between evidence publication and use in policy warrants further investigation.

Abstract Since citation context information has entered the Web of Science (Clarivate) in recent years, one wonders whether the legitimacy and soundness of citation analyses for measuring research quality of publications can be improved by considering this information. The legitimacy and soundness of citation analysis can be established by assessing their construct validity. This can be done by studying how citation impact variables relate to generally accepted external validation data, like human expert assessments of research quality. In this study, we investigated the statistical associations of information extracted from structured citation context data – such as the position of citations in certain sections – with post-publication peer quality ratings from Faculty Opinions (H1 connect) as external validity criterion data for publications in medicine and life sciences. We found that quantitative variables derived from citation contexts are correlated with human expert assessments over and above ordinary citation counts. Hence, the construct validity of citation analyses can be improved by incorporating citation context data into measurement models. The legitimacy and soundness of citation analyses for measuring research quality can be improved by considering this information (from the Web of Science).

Although the significance of academic research in policymaking is increasing, empirical examination of elite researchers' contributions to policy and the related knowledge disparities, especially in communication, is still insufficient. This study examines the policy impact of International Communication Association (ICA) Fellows, evaluating data from 288 Fellows (August 2024) using the Overton database, categorized by type of policy institution (government, intergovernmental organizations, Think tanks). Findings indicate a substantial U.S. institutional predominance (69%) among highly prominent Fellows. Overall policy influence correlated positively with IGO influence (r=.22, p<.05) but not with government or Think tank influence; negative correlations (p<.01) between different institution types suggest differentiated influence pathways. A t-test showed U.S.-affiliated scholars had significantly higher influence on government policy reports than non-U.S. counterparts [t(127)=2.36, p=.020], empirically highlighting U.S.-centrism and potential knowledge imbalance in policy citations. This research offers foundational data for science-policy interface studies by segmenting elite scholars' policy influence. Limitations, including absent citation context analysis and non-U.S. sample constraints, call for future research.

Purpose This study aims to address the challenge of efficiently identifying appropriate citations in scientific literature, which has become increasingly difficult due to the rapid growth in the volume of scientific research. This study aims to enhance citation recommendation systems by incorporating both textual and bibliographic network information. Design/methodology/approach A novel attention-based encoder-decoder model is proposed, which integrates textual information within citation contexts and bibliographic network information outside of these contexts. The model uses a graph representation learning method to capture complex citation and co-author relationships within a bibliographic network. Additionally, academic pre-trained language models are utilized within an encoder-decoder framework to generate rich representations of citation contexts. Findings Extensive experiments on a real-world dataset demonstrate the effectiveness of the proposed method. Comparative experiments reveal that incorporating bibliographic network information significantly improves the performance of local citation recommendations. Originality/value This study is original in its approach to combining bibliographic network information with textual context for citation recommendation. By leveraging both types of information, the proposed method addresses a gap in existing citation recommendation systems, which often overlook the importance of external bibliographic relationships.

Exploring scientific contributions through citation context and division of labor

Compared to feature engineering, deep learning approaches for citation context analysis have yet fully leveraged the myriad of design options for modeling in-text citation, citation sentence, and citation context. In fact, no single modeling option universally excels on all citation function classes or annotation schemes, which implies the untapped potential for synergizing diverse modeling approaches to further elevate the performance of citation context analysis. Motivated by this insight, the current paper undertook a systematic exploration of ensemble methods for citation context analysis. To achieve a better diverse set of base classifiers, I delved into three sources of classifier diversity, incorporated five diversity measures, and introduced two novel diversity re-ranking methods. Then, I conducted a comprehensive examination of both voting and stacking approaches for constructing classifier ensembles. I also proposed a novel weighting method that considers each individual classifier’s performance, resulting in superior voting outcomes. While being simple, voting approaches faced significant challenges in determining the optimal number of base classifiers for combination. Several strategies have been proposed to address this limitation, including meta-classification on base classifiers and utilising deeper ensemble architectures. The latter involved hierarchical voting on a filtered set of meta-classifiers and stacked meta-classification. All proposed methods demonstrate state-of-the-art results on, with the best performances achieving more than 5 and 4% improvements on the 11-class and 6-class schemes of citation function classification and by 3% on important citation screening. The promising empirical results validated the potential of the proposed ensembling approaches for citation context analysis.

PurposeThe assessment of citation value necessitates considering the number of citations and the specific characteristics of in-text citations. This article establishes the significance of location and contextual meaning attributes in determining the citation value. It also aims to create a framework for assessing the quality score metric of citations.Design/methodology/approachThe ground truth weights are created using expert opinion through the best-worst method (BWM). Automatic weighting is required for future metric development. Therefore, ground truth weights are compared to five objective multi-criteria decision-making (MCDM) methods: (1) standard deviation, (2) Gini coefficient, (3) entropy, (4) criteria importance through inter-criteria correlation (CRITIC) and (5) method based on criteria removal effects (MEREC). The selected weights are then utilized to create the metric framework.FindingsThe highest ground truth weight for the citation attributes is in the method section location (0.2176), positive sentiment (0.4455), role source from the method (0.3537) and function as a comparison (0.2554). The objective method closest to the ground truth is MEREC. Three types of citation quality score (CQS) metrics are developed to assess the significance of citation in a single appearance in a text, within a citing document and across all available data. Notably, a significant correlation exists between proposed metrics and expert judgment.Originality/valueThis article presents a novel citation metric that prioritizes quality factors over traditional metrics, focusing solely on numbers. Comparing various MCDM approaches is a novel approach to scientific measurement research.

This systematic review evaluates the accuracy of citations to a landmark study on premature mortality among autistic people.

Purpose This study aims to examine Erving Goffman’s contributions to management, arguing that he is an unrecognized management guru despite being widely regarded as a sociological theorist. Design/methodology/approach Using citation context analysis, this research analyzes 806 articles citing Goffman’s works across eight major management journals. This method involves coding articles from various perspectives, including the content itself, its temporal dynamics, depth and criticalness. Findings All 11 of Goffman’s books have been cited in management studies with increasing frequency and depth, supporting theories such as impression management and stigma management. Yet, only 10.8% of these articles provide empirical support, and 1.6% challenge his ideas, indicating a ritualistic reverence rather than rigorous scrutiny of his theories in management scholarship. Research limitations/implications This study excludes other high-quality journals and involves subjective judgment in coding. In addition, this study’s insights into Goffman’s selective attention and growing prominence remain speculative. Future research could broaden journal coverage, survey scholars’ citation motivations, and apply a difference-in-differences approach to identify causal factors. Social implications Goffman’s concepts of stigma, impression management and framing are frequently cited by management scholars, reflecting societal concerns for marginalized groups and a quest for authenticity, thus prompting deeper exploration of Goffman’s seminal works. Originality/value To the best of the authors’ knowledge, this is the first study to empirically analyze his impact on management, offering new insights into his influence in the field.