Citation Context Research Articles

Citation Concept Analysis (CCA) of concepts introduced by Pierre Bourdieu: Measuring their impact across fields and periods

ABSTRACT Purpose Citations can be used in evaluative bibliometrics to measure the impact of papers. However, citation analysis can be extended by a multi-dimensional perspective on citation impact which is intended to receive more specific information about the kind of received impact. Design/methodology/approach Bornmann, Wray, and Haunschild (2019) introduced citation concept analysis (CCA) for capturing the importance and usefulness certain concepts have in subsequent research. The method is based on the analysis of citances – the contexts of citations in citing papers. This study applies the method by investigating the impact of various concepts introduced in the oeuvre of the world-leading French sociologist Pierre Bourdieu. Findings We found that the most cited concepts are ‘social capital’ (with about 34% of the citances in the citing papers), ‘cultural capital’, and ‘habitus’ (both with about 24%). On the other hand, the concepts ‘doxa’ and ‘reflexivity’ score only about 1% each. Research limitations The formulation of search terms for identifying the concepts in the data and the citation context coverage are the most important limitations of the study. Practical implications The results of this explorative study reflect the historical development of Bourdieu’s thought and its interface with different fields of study. Originality/value The study demonstrates the high explanatory power of the CCA method.

Why do some retracted articles continue to get cited?

Retracted publications can still receive a substantial number of citations after the retraction. Little is known about the causes for this phenomenon and the nature of epistemic risk or harm in these retraction cases. Using this phenomenon also as an example for the broader question of how scholarly communities deal with uncertainty in the reception of publications, this case study aims to assess the epistemic contributions of retracted publications with continuous and decreasing citation impact and to relate these to the epistemic environments of the retracted papers and to reception patterns. Several parsing and natural language processing approaches are used, complemented, and validated by qualitative close reading. Specifically, (i) dissent and support are identified in citing and in co-cited publications; (ii) the concept terms of retracted publications are expanded by word embeddings and MeSH terms and traced in citation contexts; and finally (iii) rhetorical functions in citation contexts are identified based on keyword extraction. Empirical support and unresolved disputes are found almost exclusively in cases with continuous citations. Authors emphasize specific informational values in some cases with citations continuing after the retraction, while methodological and more general levels of claims prevail in others. Citations can be meaningfully examined by considering the weighing process between epistemic risks and informational value; persistent citation impact thus doesn’t necessarily indicate the perpetuation of epistemic harm.

Dimensions: Calculating disruption indices at scale

Abstract Assessing the disruptive nature of a line of research is a new area of academic evaluation that moves beyond standard citation-based metrics by taking into account the broader citation context of publications or patents. The “CD index” and a number of related indicators have been proposed in order to characterize the disruptiveness of scientific publications or patents. This research area has generated a lot of attention in recent years, yet there is no general consensus on the significance and reliability of disruption indices. More experimentation and evaluation would be desirable, but it is hampered by the fact that the calculation of these indicators is time-consuming, especially if done at scale on large citation networks. We present a novel SQL-based method to calculate disruption indices for the Dimensions publications data on Google BigQuery. This reduces the computational time taken to produce such indices by an order of magnitude, as well as making available such functionalities within an online environment that requires no setup efforts. We explain the novel algorithm and describe how its results align with preexisting implementations of disruption indicators. This method will enable researchers to develop, validate, and improve disruption models more quickly and with more precision.

Assessing citation integrity in biomedical publications: corpus annotation and NLP models.

Citations have a fundamental role in scholarly communication and assessment. Citation accuracy and transparency is crucial for the integrity of scientific evidence. In this work, we focus on quotation errors, errors in citation content that can distort the scientific evidence and that are hard to detect for humans. We construct a corpus and propose natural language processing (NLP) methods to identify such errors in biomedical publications. We manually annotated 100 highly-cited biomedical publications (reference articles) and citations to them. The annotation involved labeling citation context in the citing article, relevant evidence sentences in the reference article, and the accuracy of the citation. A total of 3063 citation instances were annotated (39.18% with accuracy errors). For NLP, we combined a sentence retriever with a fine-tuned claim verification model to label citations as ACCURATE, NOT_ACCURATE, or IRRELEVANT. We also explored few-shot in-context learning with generative large language models. The best performing model-which uses citation sentences as citation context, the BM25 model with MonoT5 reranker for retrieving top-20 sentences, and a fine-tuned MultiVerS model for accuracy label classification-yielded 0.59 micro-F1 and 0.52 macro-F1 score. GPT-4 in-context learning performed better in identifying accurate citations, but it lagged for erroneous citations (0.65 micro-F1, 0.45 macro-F1). Citation quotation errors are often subtle, and it is currently challenging for NLP models to identify erroneous citations. With further improvements, the models could serve to improve citation quality and accuracy. We make the corpus and the best-performing NLP model publicly available at https://github.com/ScienceNLP-Lab/Citation-Integrity/.

Influence of Dervin’s sensemaking methodology determined through citation context analysis, content analysis and bibliometrics

PurposeThis study explored the influence of Dervin’s sensemaking methodology (SMM).Design/methodology/approachCitation context analysis was used to identify the most influential SMM concepts in 948 articles citing 34 SMM-related studies by Dervin that were published between 1983 and 2017. Moreover, the bibliometric method and content analysis were incorporated to examine the disciplines and research topics influenced by the SMM-related studies and the role of cited content in SMM-related studies.FindingsThe influence of SMM is concentrated in information behavior research in the field of library and information science (LIS). The 1992 book chapter From the mind’s eye of the user was most frequently cited, followed by the first SMM study from 1983; 14 of the 18 content categories were relevant to SMM. “Sensemaking,” at the core of SMM, was the most influential cited concept, primarily cited from the 1983 SMM-related study. Although the SMM was developed as a research method, it has not been primarily applied to design research methods in other studies.Originality/valueThis study explored the interdisciplinary influence of Dervin’s SMM from several aspects and demonstrated the complex information dynamics between SMM-related works and citing articles.

A Deep Multi-Tasking Approach Leveraging on Cited-Citing Paper Relationship For Citation Intent Classification

Citations are crucial artifacts to provide additional information to the reader to comprehend the research under concern. There are different roles that citations play in scientific discourse. Correctly identifying the intent of the citations finds applications ranging from predicting scholarly impact, finding idea propagation, to text summarization. With the rapid growth in scientific literature, the need for automated methods to classify citations is now growing intense. However, we can only fully understand the intent of a citation if we look at the citation context in the citing paper and also the primary purpose of the cited article. In this work, we propose a neural multi-task learning framework that harnesses the structural information of the research papers and the cited paper’s information for the effective classification of citation intents. We analyze the impact of three auxiliary tasks on the performance of our approach for citation classification. Our experiments on three benchmark citation classification datasets show that incorporating cited paper information (title) shows that our deep neural model achieves a new state-of-the-art on the ACL-ARC dataset with an absolute increase of 5.3% in the F1 score over the previous best model. We also achieve comparable performance with respect to the best-performing systems in the SDP 2021 3C Shared task on Citation Context Classification. We make our codes available at https://github.com/Tirthankar-Ghosal/citationclassification-SCIM

Exploring the scientific impact of negative results

Negative results are a routine part of the scientific research journey, yet they often receive insufficient attention in scientific publications. In this study, we investigate the scientific impact of negative results by comparing the citations and citation context between negative and positive results. Specifically, we compared 159 negative result papers from three journals: Journal of Negative Results in BioMedicine, PLoS One, and BMC Research Notes, with 1,058 matched positive result papers authored by the same first and corresponding authors. The citation context was categorized according to three dimensions: citation aspect, citation purpose, and citation polarity. The first two were automatically provided by Citation Opinion Retrieval and Analysis (CORA), while citation polarity was manually annotated. Our analysis revealed several key findings. Firstly, negative results received 38.6 % fewer citations than positive results, even after controlling for bibliographic factors. Secondly, negative results were associated with a significantly higher proportion of negative citations when compared to positive results. Lastly, a higher proportion of negative results were negatively cited in the methods section.

TRENDS IN THE SCIENTIFIC LITERATURE ON THE LEAGILE SUPPLY CHAIN: BIBLIOMETRIC AND CITATION CONTEXT ANALYSES

TRENDS IN THE SCIENTIFIC LITERATURE ON THE LEAGILE SUPPLY CHAIN: BIBLIOMETRIC AND CITATION CONTEXT ANALYSES

A fast local citation recommendation algorithm scalable to multi-topics

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time.

Neural machine translation for in‐text citation classification

AbstractThe quality of scientific publications can be measured by quantitative indices such as the h‐index, Source Normalized Impact per Paper, or g‐index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full‐text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning‐based architecture for citation context classification. Unlike feature‐based state‐of‐the‐art models, our proposed focal‐loss and class‐weight‐aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state‐of‐the‐art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.

Modelling in engineering: A citation context analysis

Purely quantitative citation measures are widely used to evaluate research grants, to compare the output of researcher or to benchmark universities. The intuition that not all citations are the same, however, can be illustrated by two examples. First, studies have shown that erroneous or controversial papers have higher citation counts. Second, does a high-level citation in an introduction have the same impact as a reference to a paper that serves as a conceptual starting point? Companions to purely quantitative measures are the so-called citation context analyses which aim to obtain a better understanding of the link between citing and cited work. In this article, we propose a classification scheme for citation context analysis in the field of modelling in engineering. The categories were defined based on an extensive literature review and input from experts in the field of modelling. We propose a detailed scheme with six categories ( Perfunctory, Background Information, Comparing/Confirming, Critique/Refutation, Inspiring, Using/Expanding) and a simplified scheme with three categories ( High-level, Critical Analysis, Extending) that can be used within automatic classification approaches. The results of manually classifying 129 randomly selected citations show that 87% of citations fall into the high-level category. This study confirms that critical citations are not common in written academic discourse, even though criticism is essential for scientific progress and knowledge construction.

A History of Conceptual Parallax. A Study on the Mutual Influence between the Works of Wundt and Freud

Wilhelm Wundt and Sigmund Freud are the founders of psychological modern thought. The concept citation context method was used to analyse the mutual influence. The results show that Wundt's work was important to explain the hallucinatory character of the dream. On the other side, Wundt's quotations about Freud show his refusal to recognize the unconscious as a valid hypothesis to explain any kind of psychological phenomenon. In conclusion, the Freudian unconscious is the excess of the lack of the Wundt´s system to explain phenomena that exceed the study of normal consciousness.

A Systematic Review of Citation Recommendation Over the Past Two Decades

A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.

“The Gloss of History”: A Historical Analysis of U.S. Supreme Court Justices’ Framing of First Amendment Press Rights to Cover and Access Court Proceedings

Originalism explores the intentions for, or understandings of, constitutional rights held by drafters of the Constitution or founders of the United States. This qualitative historical analysis evaluates the accuracy and adequacy of U.S. Supreme Court justices’ citations of founders’ intentions for, or understandings of, free press rights in opinions addressing journalists’ rights to cover and access court proceedings. Our research found that justices some times have accurately and adequately cited founders’ writings. Yet, justices too often have cited court opinions and other documents from the 1800s or 1900s to support their assertions about original intentions for or understandings of freedom of speech or of the press and First Amendment values. When referencing writings by founders, justices sometimes provided inadequate context. Such practices have harmed the accuracy of legal and historical records. To improve accuracy in future opinions, justices need to stop citing sections of previous opinions that lack accurate citation, adequate citation, or proper context.

OpCitance: Citation contexts identified from the PubMed Central open access articles

OpCitance contains all the sentences from 2 million PubMed Central open-access (PMCOA) articles, with 137 million inline citations annotated (i.e., the “citation contexts”). Parsing out the references and citation contexts from the PMCOA XML files was non-trivial due to the diversity of referencing style. Only 0.5% citation contexts remain unidentified due to technical or human issues, e.g., references unmentioned by the authors in the text or improper XML nesting, which is more common among older articles (pre-2000). PubMed IDs (PMIDs) linked to inline citations in the XML files compared to citations harvested using the NCBI E-Utilities differed for 70.96% of the articles. Using an in-house citation matcher, called Patci, 6.84% of the referenced PMIDs were supplemented and corrected. OpCitance includes fewer total number of articles than the Semantic Scholar Open Research Corpus, but OpCitance has 160 thousand unique articles, a higher inline citation identification rate, and a more accurate reference mapping to PMIDs. We hope that OpCitance will facilitate citation context studies in particular and benefit text-mining research more broadly.

Citation culture: Citing authors behaviour vs trust in research results

The importance of correct citation for the development and deepening of scholarly knowledge is evident. At the same time, considering citation in assessing the quality level of a particular paper or journal can be treated differently due to citation practices, thus requiring a systematic analysis, and tracking of their development. The purpose of this article is to present the architecture of the citation landscape current state and comment on steps to avoid common pitfalls in the citation process. The authors have analyzed the most significant foreign studies on the topic, highlighted the main key aspects of the modern citation culture and new trends that can change the citation practice. A typology of citation is offered, the most prominent theories of citing authors behavior are commented, strategies of citing authors behavior and factors that determine the characteristics of citation are described, parameters of high-quality citation are analyzed, and recommendations for the implementation of successful strategies for citing authors behavior are given. The authors have also highlighted new trends in the context of citation (review of the citation context, the phenomenon of unwanted citation) that can significantly transform the citation practice. Understanding the landscape of modern citation culture by citing authors is fundamentally capable of optimizing citation standards, achieving maximum transparency and validity of the scholarly communication context.

Correction: How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities

Correction: How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

AbstractMain path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities

How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities

Psychological experiments on student self-government: The early impact of Wilhelm Mann's work in Chile and the German Empire.

One of the most important successes in the history of psychology in Chile was the foundation in 1908 of the first experimental psychology laboratory in Santiago by the German psychologist Wilhelm Mann (1974-1943). Four years later, Mann give a shift to his classical experimental psychology research to intervene in the discussions about German School Reform (1900-1920). Mann used Chile as a "testing ground" for explore the viability of student self-government published in three papers. The method used to verify the early impact of Mann's papers was the quantitative analysis of citations with Publish or Perish software using a Google Books database and Scripta Paedagogica. The reception of Mann's texts was analyzed using the context of citation and the functions and use of those citations. The three unknow Mann's papers about Student Self-Government published in 1913 and his citations. The results shows that Mann's critics and recommendations published in one of his papers was the fourth more citated in a database of 16 foundational German works of to self-student government. Finally, this Mann's article was cited and used in an ideological way to argue in favor of reactionary and conservative opinions of school democratization in German Empire teacher circles. Mann's diagnosis and critical suggestions was recognized by prominent German philosophers and pedagogues. Precisely Mann criticized the Student Republics as the only way to stimulate the student self-government for their artificial character and especially for the loss of students' psychological individuality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).