In the talk, illustrated by the slides below, Chaoqun uses bibliometric data to analyze the sociality, equality, and dynamicity of the scientific workforce.
In her abstract, Chaoqun describes her argument as follows:
I argue that, for a country to be scientifically competitive, it must maximize its human intellectual capital-base and support this workforce equitably and efficiently. I propose here a large-scale and heterogeneous analysis of the sociality, equality, and dynamicity of the scientific workforce through novel computational models for understanding and predicting the career trajectory of scientists based on their transformative interactions, gender, and levels of funding. This analysis will be able to isolate factors that contribute to the health and well-being of the scientific workforce. The computational models will quantify the impact of those transformative events and interactions and provide models to predict the career trajectory of scientists based on their gender, the size and position of the social network, and other demographic factors.
According to the talk, there are three types of events that are particularly likely to transform scholarly careers: being mentored, publishing, and receiving grants. Of these, mentoring occurs earliest in a scholar’s career and has a persistent effect on publication and grants. The relationship is not simple and automatic — mentees do not automatically inherit their mentors success in publication and grant funding. Instead the mentoring relationship is mediated by transfer of knowledge, norms, advice, and connections. And gender disparities are persistent and visible.
This talk resonated with a number of areas in which the Program and Library engage:
First, diversity is a core library value, and this research suggests ways in which the libraries can support a more diverse academic community. The success of early career scholars depends in part on developing a substantial number of specialized career skills that are not part of a specific scientific discipline — including, among many other things (see for example, these slides on reputation and communication), navigating the scholarly publishing process, writing grant proposals, managing bibliographies, and curating data. Much of this knowledge is tacit — it is not explicitly taught but instead transferred through personal mentoring. Libraries are one of the rare parts of the university that are able to successfully capture this tacit knowledge and make it more widely available across the community. The libraries IAP courses are an excellent example of this.
Second, most of the data used for this research is based on Library-mediated collections — citations drawn from journal collections and metadata from dissertation collections. Further, as there is increasing pressure on universities for quantitative evaluation, and increasing desire to actively catalyze collaboration and productivity, there is an increasing need for rich access to Library collections as data, for guidance on tools and approaches (see, for an overview our class on citation analysis), and for expert assistance. Since few researchers have methodological or domain expertise related to bibliometric and scientometric data, this presents an unusual opportunity for libraries to be entrepreneurial in collaborating on new research.
Third, during this talk, Chaoqun noted that that the most laborious and time-consuming phase of the research was the data cleaning and linking phase — particularly dealing with name disambiguation. ORCID, in which the library serves a leadership role (and which MIT has adopted), aims to eliminate this problem. ORCID has spread widely — and just within this month over a dozen major publishers announced their intent to require ORCID’s for journal submissions.