Doctoral studies play a vital role in socializing young individuals in science as they navigate the challenges of modern knowledge-based societies. Taking various forms and intensities, the ...mentor–mentee relationship is integral to this process. The paper therefore addresses the temporal patterns of scientific collaboration between mentors and mentees, as well as among co-mentors, given that understanding the characteristics of mentoring collaborations is essential for developing successful higher education strategies for attracting potential doctoral students and designing effective science policies. Patterns of collaboration were identified using a symbolic data clustering approach and described using linear discriminant analysis. The data come from national information systems in Slovenia and cover the period between 1991 and 2020. On the mentor–mentee level, six types of scientific collaborations were identified and classified in three broader categories:
study-limited
,
already established
, and
born and raised
. The most common,
born and raised,
is characterized by students who are isolated from the scientific community at the beginning of their studies and have become well integrated into the scientific community and highly productive researchers by the time they complete their doctoral studies. The
study-limited
collaboration pattern is becoming increasingly popular and more common in the natural and technical sciences. The
already established
is more common among older mentees. The notion that mentoring promotes long-term scientific collaboration between mentors is not confirmed by the presented study. However, higher levels of collaboration between mentors are associated with younger age, working in the same scientific discipline, and younger mentors.
Population ageing requires society to adjust by ensuring additional types of services and assistance for elderly people. These may be provided by either organized services and sources of informal ...social support. The latter are especially important since a lack of social support is associated with a lower level of psychological and physical well-being. During the Covid-19 pandemic, social support for the elderly has proven to be even more crucial, also due to physical distancing. Therefore, this study aims to identify and describe the various types of personal social support networks available to the elderly population during the pandemic. To this end, a survey of Slovenians older than 64 years was conducted from April 25 to May 4, 2020 on a probability web-panel-based sample (n = 605). The ego networks were clustered by a hierarchical clustering approach for symbolic data. Clustering was performed for different types of social support (socializing, instrumental support, emotional support) and different characteristics of the social support networks (i.e., type of relationship, number of contacts, geographical distance). The results show that most of the elderly population in Slovenia has a satisfactory social support network, while the share of those without any (accessible) source of social support is significant. The results are particularly valuable for sustainable care policy planning, crisis intervention planning as well as any future waves of the coronavirus.
The development and successful implementation of R&D policies depends on understanding patterns of scientific collaboration (SC). Existing studies on SC typically focus on the individual level, ...despite SC occurring on many interdependent social levels. Therefore, this paper provides a simultaneous insight into SC patterns among researchers (individual level) and among organizations (organizational level) in the social sciences. SC on the individual level is operationalized by co-authorship of a scientific paper whereas two organizations are said to collaborate if they share a research project. Based on data for the period 2006–2015 retrieved from Slovenian national information systems, two-level collaboration networks were formed with respect to researchers in the social sciences field. These networks were analyzed using a k-means-based blockmodeling approach for linked networks. The results show a high level of interdisciplinary SC and a large organizational impact on individual collaborations. On the individual level, a structure with several cohesive clusters and a semi-periphery appears while, on the organizational level, a kind a core–periphery structure emerges in which both the core and periphery can be split into several clusters. The most surprising result indicates that SC on the level of organizations is often not reflected in common published scientific papers on the individual level (and vice versa).
Researchers have extensively studied the social mechanisms that drive the formation of networks observed among preschool children. However, less attention has been given to global network structures ...in terms of blockmodels. A blockmodel is a network where the nodes are groups of equivalent units (according to links to others) from a studied network. It is already shown that mutuality, popularity, assortativity, and different types of transitivity mechanisms can lead the global network structure to the proposed asymmetric core-cohesive blockmodel. Yet, they did not provide any evidence that such a global network structure actually appears in any empirical data. In this paper, the symmetric version of the core-cohesive blockmodel type is proposed. This blockmodel type consists of three or more groups of units. The units from each group are internally well linked to each other while those from different groups are not linked to each other. This is true for all groups, except one in which the units have mutual links to all other units in the network. In this study, it is shown that the proposed blockmodel type appears in empirical interactional networks collected among preschool children. Monte Carlo simulations confirm that the most often studied social network mechanisms can lead the global network structure to the proposed symmetric blockmodel type. The units' attributes are not considered in this study.
Understanding the patterns and underlying mechanisms that come into play when employees exchange their knowledge is crucial for their work performance and professional development. Although much is ...known about the relationship between certain global network properties of knowledge-flow networks and work performance, less is known about the emergence of specific global network structures of knowledge flow. The paper therefore aims to identify a global network structure in blockmodel terms within an empirical knowledge-flow network and discuss whether the selected local network mechanisms are able to drive the network towards the chosen global network structure. Existing studies of knowledge-flow networks are relied on to determine the local network mechanisms. Agent-based modelling shows the selected local network mechanisms are able to drive the network towards the assumed hierarchical global structure.
This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), ...considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks' blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account.
A blockmodel is a network in which the nodes are clusters of equivalent (in terms of the structure of the links connecting) nodes in the network being studied. The term block refers to the links ...between two clusters. When structural equivalence is relied on, two types of blocks are possible: complete blocks and null blocks. Ideally, all possible links are found in complete blocks while there are no links in null blocks. Yet, in the case of empirical networks, some links frequently appear in null blocks and some non-links appear in complete blocks. These links and non-links are called inconsistencies. When a relocation algorithm is applied to obtain a blockmodel, the criterion function is minimised. The number of inconsistencies is reflected in a criterion function’s value, leading to it being regularly used to fit an empirical network to an ideal blockmodel. Since the value of a criterion function depends on various factors (e.g. the block types allowed, the network size and its density), the values obtained for different networks are incomparable. To address this deficiency, the Relative Fit measure is proposed in this paper. Relative Fit values may be used to select the appropriate blockmodel type and/or number of clusters. Values of the Relative Fit measure can also be of value when fitting different empirical networks to a given blockmodel.
Objective
Informational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in ...the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums.
Methods
A multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS.
Results
The results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P < .001), trustworthiness (M = 4.65, P < .001), relevance (M = 3.59, P < .001), and justification (M = 3.81, P = .05) in messages providing informational social support regarding cancer-related issues.
Conclusions
This study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.
Blockmodeling refers to a variety of statistical methods for reducing and simplifying large and complex networks. While methods for blockmodeling networks observed at one time point are well ...established, it is only recently that researchers have proposed several methods for analysing dynamic networks (i.e., networks observed at multiple time points). The considered approaches are based on k-means or stochastic blockmodeling, with different ways being used to model time dependency among time points. Their novelty means they have yet to be extensively compared and evaluated and the paper therefore aims to compare and evaluate them using Monte Carlo simulations. Different network characteristics are considered, including whether tie formation is random or governed by local network mechanisms. The results show the Dynamic Stochastic Blockmodel (Matias and Miele 2017) performs best if the blockmodel does not change; otherwise, the Stochastic Blockmodel for Multipartite Networks (Bar-Hen et al. 2020) does.
•We consider dynamic networks, i.e., networks measured at multiple time points.•We show which blockmodeling approach is preferred in different conditions.•Blockmodel stability should be considered while selecting a blockmodeling approach.•A larger network and greater differences among block densities produce better results.
PDF
