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Citation networks enable analysis of author groups, defining in-group dynamics, and mapping out inter-group relationships. While intellectual diversity and inclusiveness is one of the important principles of modern scholarship, it is intriguing to explore the extent to which these principles apply to historical communities of leaders and intellectuals. This paper introduces a novel methodological framework aimed at assessing the degree of viewpoint plurality and diversity of historical scholarship communities, through an in-depth analysis of the citations used in their literature, which has become possible due to the recently developed advanced computational analysis techniques. To achieve this goal, we have devised a set of new network-based indicators grounded in standard network metrics. These indicators can be applied at both the individual author and community levels. The developed methodology was applied to a citation network automatically constructed from a corpus of Rabbinic Halachic literature spanning the 10th to 15th centuries. This corpus includes over 5,000 citations from hundreds of books authored by approximately 140 Rabbinic scholars from six diverse geographic communities. We found that most of the authors and communities cite many more external resources from other communities than their own reflecting a willingness to engage with a diverse range of viewpoints. A more in-depth analysis based on the novel proportional diversity measures unveils more intriguing insights. Contrary to expectations, communities with the greatest number of external citations, such as Spain and Ashkenaz, surprisingly exhibit lower levels of viewpoint plurality compared to others, such as Italy and North Africa, elucidating a key finding of the study.

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Inclusive citizen science, an emerging field, has seen extensive research. Prior studies primarily concentrated on creating theoretical models and practical strategies for diversifying citizen science (CS) projects. These studies relied on ethical frameworks or post-project empirical observations. Few examined active participants' socio-demographic and behavioral data. Notably, none, to our knowledge, explored prospective citizen scientists' traits as intrinsic factors to enhance diversity and engagement in CS. This paper presents a new inclusive CS engagement model based on quantitative analysis of surveys administered to 540 participants of the dedicated free informal education MOOC (Massive Open Online Course) 'Your Right to Privacy Online' from eight countries in the EU funded project, CSI-COP (Citizen Scientists Investigating Cookies and App GDPR compliance). The surveys were filled out just after completing the training stage and before joining the project as active CSs. Out of the 540 participants who completed the surveys analyzed in this study, only 170 (32%) individuals actively participated as CSs in the project. Therefore, the study attempted to understand what characterizes these participants compared to those who decided to refrain from joining the project after the training stage. The study employed descriptive analysis and advanced statistical tests to explore the correlations among different research variables. The findings revealed several important relationships and predictors for becoming a citizen scientist based on the surveys analysis, such as age, gender, culture, education, Internet accessibility and apps usage, as well as the satisfaction with the MOOC, the mode of training and initial intentions for becoming a CS. These findings lead to the development of the empirical model for inclusive engagement in CS and enhance the understanding of the internal factors that influence individuals' intention and actual participation as CSs. The devised model offers valuable insights and key implications for future CS initiatives. It emphasizes the necessity of targeted recruitment strategies, focusing on underrepresented groups and overcoming accessibility barriers. Positive learning experiences, especially through MOOCs, are crucial; enhancing training programs and making educational materials accessible and culturally diverse can boost participant motivation. Acknowledging varying technological proficiency and providing necessary resources enhances active engagement. Addressing the intention-engagement gap is vital; understanding underlying factors and creating supportive environments can transform intentions into active involvement. Embracing cultural diversity through language-specific strategies ensures an inclusive environment for effective contributions.

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One of the main concerns of researchers and institutions is how to assess the future performance of scholars and identify their potential to become successful scientists. In this study, we model scholarly success in terms of the probability of a scholar belonging to a group of highly impactful scholars as determined by their citation trajectory structures. To this end, we developed a new set of impact measures based on a scholar's citation trajectory structure (rather than on absolute citation or h-index rates), that show a stable trend and scale for highly impactful scholars, independent of their field of study, seniority and citation index. These measures were then incorporated as influence factors into the logistic regression models and used as features for probabilistic classifiers based on these models to identify the successful scholars in the heterogeneous corpus of 400 of most and least cited professors from two Israeli universities. From the practical point of view, the study may yield useful insights and serve as an aid in making promotion decisions by institutions, as well as a self-assessment tool for researchers who strive to increase their academic influence and become leaders in their field.

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Previous research shows that users tend to change their assessment of search results over time. This is a first study that investigates the factors and reasons for these changes, and describes a stochastic model of user behaviour that may explain these changes. In particular, we hypothesise that most of the changes are local, i.e. between results with similar or close relevance to the query, and thus belong to the same"coarse" relevance category. According to the theory of coarse beliefs and categorical thinking, humans tend to divide the range of values under consideration into coarse categories, and are thus able to distinguish only between cross-category values but not within them. To test this hypothesis we conducted five experiments with about 120 subjects divided into 3 groups. Each student in every group was asked to rank and assign relevance scores to the same set of search results over two or three rounds, with a period of three to nine weeks between each round. The subjects of the last three-round experiment were then exposed to the differences in their judgements and were asked to explain them. We make use of a Markov chain model to measure change in users' judgments between the different rounds. The Markov chain demonstrates that the changes converge, and that a majority of the changes are local to a neighbouring relevance category. We found that most of the subjects were satisfied with their changes, and did not perceive them as mistakes but rather as a legitimate phenomenon, since they believe that time has influenced their relevance assessment. Both our quantitative analysis and user comments support the hypothesis of the existence of coarse relevance categories resulting from categorical thinking in the context of user evaluation of search results.

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