Towards prediction of paradigm shifts from scientific literature

Prabhakaran, Thara; Lathabai, Hiran H.; George, Susan E.; Changat, Manoj

doi:10.1007/s11192-018-2931-3

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…𝑊 𝐹𝑉 𝑖 is termed as the flow vergence index by Prabhakaran et al (2015) because positive value of the index can clearly indicate works that are flow divergent and negative value of the index indicate works that are flow convergent as shown in figure 2 (right). More details about theoretical origin and rationale behind the formulation of the FV index is already covered by Prabhakaran et al (2015), Lathabai et al (2015), Lathabai et al (2017), Lathabai et al (2018) and Prabhakaran et al (2018).…”

Section: Fv Gradient Method: a Short Revisitmentioning

confidence: 99%

“…However, in some special cases, when newer works (citing works) tend to perform better than the former ones (cited ones) due to its inherent merit or innovative appeal, arcs having ∆𝐹𝑉 𝑖𝑗 < 0 can be found, making knowledge flow to appear as to have occurred from low potential work to a high potential work. This phenomenon was termed as Flow Vergence effect or FV effect and was used to detect (Lathabai et al, 2015) and predict (Prabhakaran et al, 2018) pivot papers of paradigm shift. Computation of FV gradients of all the arcs makes the network a signed weighted network and therefore FV gradient computation can be treated as a weight assignment method that probably have the ability to highlight paths that might not be highlighted through SPX methods as shown in Lathabai et al (2018).…”

Section: Fv Gradient Method: a Short Revisitmentioning

confidence: 99%

“…using the Bradford Law of scattering (Bradford, 1934;Egghe, 1990a;Oluić-Vuković, 1992), a macroscopic understanding of the field is attempted to be obtained. The study looks further into the technical evolution of knowledge of AI application domains and methodologies using Path analysis (Hummon & Doreian, 1989;Lathabai et al, 2018;Prabhakaran et al, 2018). This provides a microscopic understanding of the developments related to AI that serve as major enhancer or even has the potential to become game changer in many other areas that are closely associated with sustainable development goals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Patterns in the growth and thematic evolution of Artificial Intelligence research: A study using Bradford distribution of productivity and Path Analysis

Gupta

Kanaujia

Lathabai

et al. 2022

Preprint

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AI Applications has led to changes in social as well as economic structure of the world and caused worldwide policy changes. AI research and applications have not only increased in volume but has also expanded across various disciplines. Newer themes of research have emerged while attention on several topics has gradually faded. Therefore, there is a need to analyse and understand the patterns of growth and thematic evolution of AI research during the last decade. Motivated by the need, Bibliometric methodologies of Bradford’s law and Path analysis are employed to develop a framework for a detailed analysis. Publication and citation data of AI for the period 2010 to 2020 obtained from Web of Science when analysed using this framework, showed an exponential growth between 2016 and 2020. An increased adoption of AI in novel domains is indicated by an increase in both concentration and dispersion of the literature. Through the analysis of evolutionary trajectories contributing to growth of AI, it was observed that applications of AI are becoming common in previously unrelated areas such as water resource management, environment management, building technology, healthcare etc. These are areas which directly relate with sustainable development and the researchers have used multiple single as well as hybrid AI tools to address their problems. The article presents useful insight along these lines which can be useful for practitioners in the field of AI.

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Section: Fv Gradient Method: a Short Revisitmentioning

confidence: 99%

Section: Fv Gradient Method: a Short Revisitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Patterns in the growth and thematic evolution of Artificial Intelligence research: A study using Bradford distribution of productivity and Path Analysis

Gupta

Kanaujia

Lathabai

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Document co-citation changes through time as chronologically older documents accumulate more citations (Batistič et al, 2017). In this way, co-citation frequencies can shape a particular intellectual field and are helpful in detecting shifts in certain schools of thought (Prabhakaran et al, 2018). Document co-citation analysis can also reveal the intellectual foundations of a particular scientific domain by identifying its core works.…”

Section: Document Co-citationmentioning

confidence: 99%

The Road to the Future: A Multi-Technique Bibliometric Review and Development Projections of the Leader–Member Exchange (LMX) Research

2022

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This study examines the evolution of leader–member exchange (LMX) research. We apply bibliometrics to analyze the extant literature published from the beginning of the construct development, complementing existing qualitative and meta-analytic reviews of the LMX field. We use a combination of three bibliometric techniques—document co-citation analysis, co-word analysis, and bibliographic coupling. Our approach enables us to identify the most influential topics, determine the underlying structure and development of the field by interpreting bibliometric results against the backdrop of the invisible colleges framework, as well as detect emerging topics. We conclude by providing important theoretical and methodological implications, suggesting the emergence of new sub-fields and future opportunities for new connections among specific existing sub-streams of LMX, leadership, and management research.

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“…Ponomarev et al (2014) proposed two forecasting models for the early detection of potential TR based on the dynamics of citation counts. Based on the idea of the knowledge flow path, Prabhakaran et al (2015Prabhakaran et al ( , 2018 constructed a new model for temporal development of the network in which they claimed that nodes whose outdegree outnumbers the indegree can be regarded as transformative research. More recently, Min et al (2021) compared transformative papers with "normal" investigations and found that the citing network of TRs has globally more edges than that of 'normal' research, but the number of nodes is very similar.…”

Section: Definition and Identification Of Transformative Researchmentioning

confidence: 99%

Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?

Rousseau

Liang

et al. 2022

Scientometrics

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In this contribution, we conduct a multi-angular analysis of the interdisciplinarity of Nobel Prize winning research compared to non-Nobel Prize winning articles, based on a large data set. Here interdisciplinarity is measured by the diversity of references, using two true diversity indicators.Articles mentioned by the Nobel Prize committee in Physiology or Medicine (in short: NP articles) awarded during the period from 1900 to 2016 are the focus of our research. These articles are compared with those in a dataset of articles that do not include a Nobel Prize winner among their authors.Moreover, these non-NPs articles were not only published in the same year and in the same research field as the NP ones but were also dealing with the same research topic (such articles are referred to as non-NP articles).The results suggest that the topic-related knowledge included in Nobel Prize winning work is higher than that in non-NPs, hence with lower interdisciplinarity than the latter. Our findings provide useful clues to better understand the characteristics of transformative research, here represented 2 by key publications by Nobel Prize laureates in Physiology or Medicine, and their pattern of knowledge integration.

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Towards prediction of paradigm shifts from scientific literature

Cited by 14 publications

References 20 publications

Patterns in the growth and thematic evolution of Artificial Intelligence research: A study using Bradford distribution of productivity and Path Analysis

Patterns in the growth and thematic evolution of Artificial Intelligence research: A study using Bradford distribution of productivity and Path Analysis

The Road to the Future: A Multi-Technique Bibliometric Review and Development Projections of the Leader–Member Exchange (LMX) Research

Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?

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