In Sarajevo, since the formation of the Jewish religious community, the religious education of children has developed simultaneously. First, four-grade elementary schools, where mostly male children went, came forward. Later in the 17th century, Talmud-Torah secondary school was developed, while Yeshiva was only formed in the second half of the 18th century. Until the establishment of the Belgrade Yeshiva by Rav Yehuda Lerma in 5395 (1635) and the Sarajevo Yeshiva by Rav David Pardo in 5528 (1768), there were no rabbinical schools in the territories of the Western Balkans and neither rabbis. In the Kingdom of Serbs, Croats, and Slovenes, later the Kingdom of Yugoslavia, there was a need for qualified personnel for the religious education of Jewish children and youth according to general laws, in lower and secondary schools. On June 13, 1928, the Jewish Secondary Theological Seminary was opened, which began operating on November 25, 1928. The Seminary operated until 1941, when it was closed on April 6 by Nazzi Germans. The paper aims to present the development of Jewish religious education from the arrival of Sephardim to Sarajevo in the 16th century until 1941. To show the importance of the development of rabbinic and Talmudic studies in Bosnia and Herzegovina, as well as the reputation of Sarajevo's Jewish religious schools in Europe and the world.
PurposeThe paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates.Design/methodology/approachThis paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained.FindingsIt is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process.Research limitations/implicationsA small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles.Practical implicationsThe presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice.Social implicationsThe social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment.Originality/valueIn contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.