The Community-Engaged Lab: A Case-Study Introduction for Developmental Science

Due to the closing of campuses, museums, and other public spaces during the pandemic, the typical avenues for recruitment, partnership, and dissemination are now unavailable to developmental labs. In this paper, we show how a shift in perspective has impacted our lab's ability to successfully transition to virtual work during the COVID-19 shut-down. This begins by recognizing that any lab that relies on local communities to engage in human research is itself a community organization. From this, we introduce a community-engaged lab model, and explain how it works using our own activities during the pandemic as an example. To begin, we introduce the vocabulary of mission-driven community organizations and show how we applied the key ideas of mission, vision, and culture to discussions of our own lab's identity. We contrast the community-engaged lab model with a traditional bi-directional model of recruitment from and dissemination to communities and describe how the community-engaged model can be used to reframe these and other ordinary lab activities. Our activities during the pandemic serve as a case study: we formed new community partnerships, engaged with child “citizen-scientists” in online research, and opened new avenues of virtual programming. One year later, we see modest but quantifiable impact of this approach: a return to pre-pandemic diversity in our samples, new engagement opportunities for trainees, and new sustainable partnerships. We end by discussing the promise and limitations of the community-engaged lab model for the future of developmental research.

Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

On Multi-Flow Model of the Development in Natural Sciences

A model of the knowledge development in natural sciences named "multi-flow model” is analyzed, and two examples of this model are examined. One of them has been formulated in the article of two domestic physicists M. I. Podgoretsky and Ya. A. Smorodinsky, who have explored the applicability of the axiomatic method in physics and concluded that the construction of such axiomatic structures as in mathematics is impossible in natural sciences because, unlike mathematical theories, physical theories are incomplete. The concepts of «meeting» and «contradictions of the meeting» are formulated. It is shown how in the process of analyzing the capabilities of the axiomatic method, the authors develop the mechanism of creating a new model. This mechanism consists of building a hierarchical series of "directions", which are the vertically stacked layers of knowledge. One could hope that the emergence of such directions would enrich modern natural sciences with new opportunities and results. However, the second example developed by a prominent physicist and brilliant methodologist Carl Rovelli, reveals that everything is not so simple. In some cases, newly created non-linear models may lead to a misinterpretation of the development of scientific cognition. It is shown that the terminology used by the authors of the first example is not quite adequate to the content of the model: the main term used by them — "direction" — is not sufficiently defined. In addition, the term changes the meaning of the analysis that has been done. In this regard, it is proposed to introduce a new term "flow" and call the model itself a "multi-flow model". Not multi-directional model, but multi-flow model. In the second example of the multi-flow model, C. Rovelli uses this model to restore the unity and integrity of scientific knowledge that has been destroyed in the post-classical period of science development. This attempt to implement the new synthesis has been made by Rovelli within the framework of the quantum gravity theory.

A hyperelastic model for soils with stress-induced and inherent anisotropy

In this paper, modelling of the superposition of stress-induced and inherent anisotropy of soil small strain stiffness is presented in the framework of hyperelasticity. A simple hyperelastic model, capable of reproducing variable stress-induced anisotropy of stiffness, is extended by replacement of the stress invariant with mixed stress–microstructure invariant to introduce constant inherent cross-anisotropic component. A convenient feature of the new model is low number of material constants directly related to the parameters commonly used in the literature. The proposed description can be incorporated as a small strain elastic core in the development of some more sophisticated hyperelastic-plastic models of overconsolidated soils. It can also be used as an independent model in analyses involving small strain problems, such as dynamic simulations of the elastic wave propagation. Various options and features of the proposed anisotropic hyperelastic model are investigated. The directional model response is compared with experimental data available in the literature.

Spatial Modeling of Voter Choice: The Survey of Theoretical and Empirical Approach

As for today, political elections are the key form of people’s participation in the formation of the state in all democratic countries, which is why theoretical works in the field of spatial modeling of voter choice appeared relatively long ago and played a major role in the development of both further theoretical and empirical research in this area. In this survey we firstly give a brief overview of the history of the formation of spatial modeling in relation to election results and political preferences of individuals from the point of view of research methodology, based on the classical theoretical ‘proximity model’ and ‘directional model’, where rational individuals determine their political positions and compare them with the positions of candidates. Secondly, we explain the appearance of the studies of the mutual influence of voters living in neighboring territories on each other as one of the factors that determine the voters’ political positions and, accordingly, the final choice of a candidate. We also point out the authors’ different explanations of the reasons for the appearance of such mutual influence of voters and other factors affecting voters living in neighboring territories (also called as ‘contextual effects’) and emphasize the importance of taking them into account in the studies of electoral preferences. A separate chapter in this paper presents the systematization and description of the main empirical approaches to spatial modeling of electoral choice: at the beginning, we present the basic econometric spatial models (used by the authors regardless of the subject of the study), and then we describe the empirical work in the field of voter choice, depending on the hypotheses, focusing on the research methodology and the data used. In conclusion, we define the main directions for the research development and the vector of further practical work in this area. This paper will help researchers understand existing fundamental works, evaluate current approaches to the modeling of electoral choice, and improve theoretical or empirical spatial analysis