**5. Conclusion**

**4. Discussion**

**Table 8.** Cluster summary.

**Cluster Size Age Labels**

24 Scientometrics

is still ongoing.

**4.2. Historic footprint and emerging technologies**

**4.1. Epistemological characteristics**

The domain-level investigation revealed the following characteristics of published research in scientometrics. First, scientometrics research is multidisciplinary. Multiple disciplines such as "psychology, education, health" and "medicine, medical, clinical" are engaged in advancing knowledge in the domain. In particular, computer and information sciences had the largest influence on the emergence and development of scientific knowledge. The assignment of WoS categories also evidenced the multidisciplinarity of scientometrics as a variety of domains such as social sciences, engineering, medical and health sciences, and environmental sciences have contributed to the growth of the field. Second, scientometrics is not yet fully interdisciplinary as shown in the finding that research frontiers from "medicine, medical, clinical" largely cites from similar domains. Examining domain-level citation patterns in consideration with the WoS category assignment obtained a solid overview of the publication profile of the field. It revealed the growth of the domain by visualizing the distribution of citation trajectories at a disciplinary level, adding richer contexts with examining the distribution of WoS category assignment. Finally, most of the landmark articles were published relatively recently, namely after 2004 in spite of the long history of the domain. We argue that the domain's maturation

**LSA LLR MI** 142 2007 h-Index Major subject Productivity incentive 74 2010 References Percentile rank Average citation 47 2013 Papers Social media metrics Practical application

16 34 2008 China Processing effort Worldwide research productivity 17 32 2011 Documents Academic otolaryngologist Peer-reviewed ophthalmology 18 30 2009 Water Classic article National policy intervention

The analysis of keywords, topic models, and document clusters identified the following thematic patterns in scientometrics research. In the beginning some researchers focused on employing citation analysis to measure the impact of a science. Another effort focused on the evaluation of performance and productivity of research, employing scientometrics approaches. The identification of patterns in scientific collaboration was also among the important themes. The other effort had interest in modeling scientometrics laws and proposing scientometric indicators and impact measures. Recently, applications of scientometrics The present chapter aimed to explore epistemological characteristics, historic areas of innovation, and emerging trends in scientometrics. We achieved this by investigating domain-level citation paths, WoS category assignment, keyword co-occurrence, temporal topic models, and document clusters. The findings indicate the domain of scientometrics is multidisciplinary and partially interdisciplinary. Social sciences and biomedicine have published to the field, but not yet cited from each other. We argue that the maturation of scientometrics as a scientific field is still ongoing. Next, early studies tried to measure a science's impact and performance and productivity of published research. Successive effort investigated laws and indicators in scientometrics and explored scientific collaboration. Recent literature is paying attention to topics such as applying scientometrics approaches to different domains and bringing social media analytics in scientometrics.

The approaches of the present study provide advantages in investigating intellectual structure of a science as follows. First, we tried to make our data collection inclusive by investigating closely neighboring domains. Conventional studies of domain analysis often cover only a fraction of published literature. Our method provides a systematic way to explore the broader coverage of a scientific discipline. Second, we investigated the domain from a multi-faceted point of view. Domain-level citation trajectories, subject category assignment, networks of subject categories and keywords, bursting keywords, topic models, and document co-citation networks were identified in this study. Sub-sections in Results triangulated each other, adding richer interpretations from macro units of analysis to micro ones. Finally, the analytical procedure and tools employed in the present work enabled us to explore time-aware research trends in the domain. In addition, one can conduct this kind of domain analysis of his or her concern as frequently as needed without prior knowledge or experience. Thus, the proposed approaches have a relatively higher reproducibility and lower cost for conducting studies at a larger scale, especially as in the era of mass publication.

There are several limitations in our work. First, the topic search we conducted on WoS may have missed relevant records. It is acknowledged that the vocabulary mismatch presents a challenge for keyword-based search. We may be able to overcome this drawback by employing citation indexing or iterative search query development as an alternative strategy in order to capture a much broader context. Second, WoS as our source of data may have underrepresented conference proceedings. It is also recognized as an issue for disciplines such as social sciences and arts and humanities [13]. At the time of data retrieval, the authors' institutes only subscribed to the core collection of WoS. Thus, it was inevitable not to miss some relevant records accordingly. Additional sources such as Scopus are recommended for future refinements of this type of analysis. In addition, some findings or sub-sections in Results may seem too general to characterize emerging technologies in scientometrics when considered independently from the entire context. We argue that that is not because of the limitation of our approaches and tools but due to the characteristics of bibliographic records. That means textual fields that can be used only include titles, abstracts, and keywords which are often abstract to be inclusive. To overcome this, we employed not only frequency-based metrics such as citation counts and latent semantic analysis but also burst detection and probabilityoriented techniques such as LLR, MI, and DTM. Then, we tried to triangulate the findings from each sub-section, adding richer interpretations as moving between different units of analysis. We argue that our approaches be more strengthened if we can have access to more informational sources such as full text. Finally, we selected 100 highly cited references to generate the intellectual landscapes. Although this data reduction is in part intuitive, we can strengthen our approach by choosing cited articles based on more refined indicators such as h-index or g-index. It may be worth conducting a separate study of the theoretical implications of using a variety of conceivable selection criteria. We also plan to apply the present chapter's approaches to much more comprehensive records that cover a various type of publication materials.

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