Early Career Physical Therapy Faculty Networking and Scholarly Productivity: A Mixed-Methods Study

While it is well-known that physical therapist (PT) faculty must retain a scholarly agenda, few report being activity engaged and many programs have low scholarly dissemination. There is evidence that knowledge of the make-up of a faculty network leads to improved performance and innovation. The purpose of this explanatory sequential mixed methods study was to explore agency (behaviors and perspectives about career advancement) and the professional network structure and composition of early career PT faculty as they relate to scholarly activity. This dissertation research study included 50 early career faculty who worked in accredited entry-level physical therapy programs. The quantitative phase results showed a more open and less interconnected network is associated with higher scholarly activity when controlling for the duration as a faculty member and whether the individual has an academic doctoral degree. Agency behavior and perspective scores were not associated with higher scholarly activity. The Scholar Score developed during this phase offered a clear and uniform, peer-validated approach to account for the quantity and quality of scholarly activities. The qualitative phase used a grounded theory approach to analyze interviews with a sub-set of 20 study participants. The result was a central phenomenon of connecting with others for scholarly activity. The two constructs in the model are strategies used to develop network connections and how these connections helped faculty participate in scholarly activity. The findings about the network development process helped explain the quantitative results of high and low performers of scholarly activity. Without both study phases important information would have been missed. Key implications from this study include advancing the application of the Scholar Score and demonstrating network analysis for PT faculty. More importantly this study generated new knowledge about an effective network and the process used to create professional relationships to strengthen an early career PT faculty scholarly agenda. Network analysis made the connections visible for the early career faculty who reside at the lower end of the academic hierarchy in terms of tenure, academic rank, and scholarly productivity

Using the Co-Citation Network to Indicate Article Impact

Scholarly outputs are growing in number and frequency, driving the requirement to research new early indication metrics. Historically, citations have been used as an independent indication of the significance of scholarly material. However, citations are very slow to accrue since they can only be made by subsequently published material. This enforces a delay of a number of years before the citation impact of a publication can be accurately judged. Existing early indication metrics, such as download metrics and web based link analysis, have obtained correlation results. Brody finds a good correlation between download metrics and subsequent impact by citation, while Thelwall finds very little correlation between Google's PageRank and the number of links (or citations) to a web site, suggesting neither is a good surrogate indicator for the other. While valid studies, neither take account of the quality assessment factor of peer-review citation. This work presents an investigation into new metrics which utilize the co-citation network in order to rate a publications impact

“As-You-Go” instead of “After-the-Fact”:A network approach to scholarly communication and evaluation

Scholarly research faces threats to its sustainability on multiple domains (access, incentives, reproducibility, inclusivity). We argue that “after-the-fact” research papers do not help and actually cause some of these threats because the chronology of the research cycle is lost in a research paper. We propose to give up the academic paper and propose a digitally native “as-you-go” alternative. In this design, modules of research outputs are communicated along the way and are directly linked to each other to form a network of outputs that can facilitate research evaluation. This embeds chronology in the design of scholarly communication and facilitates the recognition of more diverse outputs that go beyond the paper (e.g., code, materials). Moreover, using network analysis to investigate the relations between linked outputs could help align evaluation tools with evaluation questions. We illustrate how such a modular “as-you-go” design of scholarly communication could be structured and how network indicators could be computed to assist in the evaluation process, with specific use cases for funders, universities, and individual researchers

Examining Scholarly Influence: A Study in Hirsch Metrics and Social Network Analysis

This dissertation research is focused on how we, as researchers, ‘influence’ others researchers. In particular, I am concerned with the notion of what constitutes the ‘influence’ of a scholar and how ‘influence’ is conferred upon scholars. This research is concerned with the construct called ‘scholarly influence’. Scholarly influence is of interest because a clear “theory of scholarly influence” does not yet exist. Rather a number of surrogate measures or concepts that are variable are used to evaluate the value of one’s academic work. ‘Scholarly influence’ is broken down into ‘ideational influence’ or the influence that one has through publication and the uptake of the ideas presented in the publication, and ‘social influence’ or the influence that one has through working with other researchers. Finally through the use of the definition of ‘scholarly influence’ this dissertation tries to commence a definition of ‘quality’ in scholarly work

El perfil de las revistas españolas de comunicación (2007-2008)

The evolution of parameters for scholarly publications and of academic requirements in Spain has resulted in changes to scholarly journals, among others, those in the field of communication sciences. This article characterizes the core Spanish communication journals according to variables such as the number of published articles, language, author institution and collaboration networks, and citation patterns during 2007-2008. By applying bibliometric techniques and social network analysis, a profi le showing similarities and differences among the journals is obtained, as well as a profile of the overall system

A principal component analysis of 39 scientific impact measures

The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution

Could scientists use Altmetric.com scores to predict longer term citation counts?

Altmetrics from Altmetric.com are widely used by publishers and researchers to give earlier evidence of attention than citation counts. This article assesses whether Altmetric.com scores are reliable early indicators of likely future impact and whether they may also reflect non-scholarly impacts. A preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is Mendeley reader counts, with weaker news, informational and social network discussion/promotion dimensions in some fields. Based on a regression analysis of Altmetric.com data from November 2015 and Scopus citation counts from October 2017 for articles in 30 narrow fields, only Mendeley reader counts are consistent predictors of future citation impact. Most other Altmetric.com scores can help predict future impact in some fields. Overall, the results confirm that early Altmetric.com scores can predict later citation counts, although less well than journal impact factors, and the optimal strategy is to consider both Altmetric.com scores and journal impact factors. Altmetric.com scores can also reflect dimensions of non-scholarly impact in some fields

Social Network Capital and Academic Careers

Social Network Capital and Academic Careers: The Case of a College of Agriculture ABSTRACT The relationship between economic performance and various forms of capital anchors a significant portion of mainstream economic theory and applied economics. Human, physical and financial capital represent important factors in the production of goods and services. The label “capital” implies characteristics such as investment, accumulation, maintenance, depreciation, and transfer. Recently, social capital or social network capital (SNC) has received increased scholarly attention in the literature of sociology, business, and economics. Limited analysis, however, has been directed at the role of SNC in the academy. We hypothesize that academic success at the professorial level is determined by the stock of human (HC) and SNC and the value flows emerging from these stocks. We view SNC as a complement to HC, increasing the productivity of HC while holding all other factors constant. An analysis of SNC’s importance to academic career success should interest the academy as well as other large organizations (i.e. research laboratories, government agencies) with similar structures and incentive systems.Social capital, academic networks, human capital, Institutional and Behavioral Economics, Labor and Human Capital, Teaching/Communication/Extension/Profession,

BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search

The enormous growth of research publications has made it challenging for academic search engines to bring the most relevant papers against the given search query. Numerous solutions have been proposed over the years to improve the effectiveness of academic search, including exploiting query expansion and citation analysis. Query expansion techniques mitigate the mismatch between the language used in a query and indexed documents. However, these techniques can suffer from introducing non-relevant information while expanding the original query. Recently, contextualized model BERT to document retrieval has been quite successful in query expansion. Motivated by such issues and inspired by the success of BERT, this paper proposes a novel approach called QeBERT. QeBERT exploits BERT-based embedding and Citation Network Analysis (CNA) in query expansion for improving scholarly search. Specifically, we use the context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance Feedback (PRF) fash-ion. Initial experimental results on the ACL dataset show that BERT-embedding can provide a valuable augmentation to query expansion and improve search relevance when combined with CNA.Comment: 1