Utilitarianism and the Westernization of Modern Mohist Studies

The term “utilitarianism” in English translates into Chinese as gongli zhuyi. When Liang Qichao and Hu Shi first imported the concept of utilitarianism into the study of Mohist thought, the term was initially translated as shili zhuyi or leli zhuyi. The use of gongli zhuyi in Mohist studies was established only through the efforts of Yan Fu and Wu Yu to break down the negative connotations of gongli in traditional Chinese culture and through the systematic research and scholarly influence of Feng Youlan. The study of Mohist thought within the framework of utilitarianism as gongli zhuyi is now common practice throughout academia with few scholars objecting to the use of this term.

ALIS: A novel metric in lineage-independent evaluation of scholars

Evaluative bibliometrics often attempts to explore various methods to measure individual scholarly influence. Scholarly independence (SI) is a unique indicator that can be used to understand and assess the research performances of individual scholars. The SI is a rare quality that most funding agencies and universities seek during funding decisions or hiring processes. We propose author lineage independent score (ALIS), a unique model to measure SI of a scholar by using his or her academic genealogy tree as the underlying graph structure. The analysis is performed on real data of 100 authors, collected from the Web of Science (WoS) and the Mathematics Genealogy Project. The analysis is further validated on a larger scale, on a simulated sample of 10,000 authors. The simulation exercise is the proof-of-concept for scalability of the metric and the proposed optimisation model. ALIS exploits genealogical relationships between scholars and their mentors and collaborating communities and constructs an influence scoring model based on the Genealogy tree structure of the respective scholars. The implications from the theoretical model are found to be profound in tracing known and recursive citation patterns among peers. The genealogy tree is used to investigate the advisor–advisee relationship and lays the foundation for defining metrics used to calculate the various indicators such as non-genealogy citations (NGCs), non-community citations (NCCs) and other citation quotient (OCQ). As these indicators/parameters are novel and thus not readily accessible, algorithms are written to compute these indicator values for the scholars under study.

Who is who in library and information science research? The integrative application of scholarly influence indicators

This research concerns determining authors’ scientific influence in library and information science research and their impact on the intellectual structure of the discipline by means of integrative indicators of the Scholarly Capital Model and co-authorship patterns. Research records comprised articles published from 1945 to 2016 in library and information science core journals and indexed in Web of Science. CiteSpace (software for visualization of scientific patterns and trends) was employed to map the intellectual structure of library and information science research based on co-authorship patterns. The results showed that the top 10 authors of library and information science research with the highest scores in terms of influence indicators (except for one person) were mostly concerned with the field of scientometrics which can be considered as the special impact of scientometric authors on the intellectual structure of library and information science research especially in recent years. Based on the results of the research, integrative use of scientometric indicators for measuring authors’ level of scholarly influence may grant a more precise perspective for decision makers in the field of library and information science.

Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes (Preprint)

BACKGROUND The eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development challenges and tasks, benchmark datasets, and software for processing and evaluation. In 2012, it ran as a scientific workshop with the aim of establishing an evaluation lab, and since 2013, this annual workshop has been supplemented with 3 or more preceding labs each year. An evaluation lab is an activity where the participating individuals or teams’ goal is to solve the same problem, typically using the same dataset in a given time frame. The overall purpose of this initiative is to support patients, their next of kin, clinical staff, health scientists, and health care policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. In the CLEF eHealth 2013 to 2017 installations, the aim was to address patient-centric text processing. From 2015, the scope was also extended to aid both patients’ understanding and clinicians’ authoring of various types of medical content. CLEF eHealth 2017 introduced a new pilot task on technology-assisted reviews (TARs) in empirical medicine in order to support health scientists and health care policymakers’ information access. OBJECTIVES This original research paper reports on the outcomes of the first 6 installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analyzing the scholarly influence by reviewing CLEF eHealth papers and their citations. METHODS A review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions were conducted. Citation content analysis was used for the publications and their citations collected from Google Scholar. RESULTS As many as 718 teams registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. A total of 184 papers using CLEF eHealth data generated 1299 citations, yielding a total scholarly citation influence of almost 963,000 citations for the 741 coauthors, and included authors from 33 countries across the world. Eight tasks produced statistically significant improvements (2, 3, and 3 times with P<.001, P=.009, and P=.04, respectively) in processing quality by at least 1 out of the top 3 methods. CONCLUSIONS These substantial participation numbers, large citation counts, and significant performance improvements encourage continuing to develop these technologies to address patient needs. Consequently, data and tools have been opened for future research and development, and the CLEF eHealth initiative continues to run new challenges.

Measuring discursive influence across scholarship

Assessing scholarly influence is critical for understanding the collective system of scholarship and the history of academic inquiry. Influence is multifaceted, and citations reveal only part of it. Citation counts exhibit preferential attachment and follow a rigid “news cycle” that can miss sustained and indirect forms of influence. Building on dynamic topic models that track distributional shifts in discourse over time, we introduce a variant that incorporates features, such as authorship, affiliation, and publication venue, to assess how these contexts interact with content to shape future scholarship. We perform in-depth analyses on collections of physics research (500,000 abstracts; 102 years) and scholarship generally (JSTOR repository: 2 million full-text articles; 130 years). Our measure of document influence helps predict citations and shows how outcomes, such as winning a Nobel Prize or affiliation with a highly ranked institution, boost influence. Analysis of citations alongside discursive influence reveals that citations tend to credit authors who persist in their fields over time and discount credit for works that are influential over many topics or are “ahead of their time.” In this way, our measures provide a way to acknowledge diverse contributions that take longer and travel farther to achieve scholarly appreciation, enabling us to correct citation biases and enhance sensitivity to the full spectrum of scholarly impact.