Comparative content analysis of master’s and doctoral programmes in demography at world-class universities

The use of big data and data analytics are slowly emerging in public policy-making, and there are calls for systematic reviews and research agendas focusing on the impacts that big data and analytics have on policy processes. This paper examines the nascent field of big data and data analytics in public policy by reviewing the literature with bibliometric and qualitative analyses. The study encompassed scientific publications gathered from SCOPUS (N = 538). Nine bibliographically coupled clusters were identified, with the three largest clusters being big data's impact on the policy cycle, data-based decision-making, and productivity. Through the qualitative coding of the literature, our study highlights the core of the discussions and proposes a research agenda for further studies. 大数据和数据分析的使用已逐渐出现在公共决策中,对此需要展开系统性综述和研究议程,聚焦大数据和数据分析对政策过程产生的影响。本文通过文献计量分析和定性分析,分析了公共政策中大数据和数据分析这一新兴领域。本研究包括了从SCOPUS中选取的科学刊物(N=538)。识别了9个文献耦合簇,其中最大的三个簇分别为:大数据对政策周期的影响、基于数据的决策、生产率。通过对文献进行定性编码,我们的研究强调了探讨的核心,并提出了进一步的研究议程。 El uso de macrodatos y análisis de datos está emergiendo lentamente en la formulación de políticas públicas, y hay pedidos de revisiones sistemáticas y agendas de investigación que se centren en los impactos que los macrodatos y el análisis tienen en los procesos de políticas. Este artículo examina el campo naciente del big data y el análisis de datos en las políticas públicas mediante la revisión de la literatura con análisis bibliométricos y cualitativos. El estudio abarcó publicaciones científicas recopiladas de SCOPUS (N = 538). Se identificaron nueve grupos acoplados bibliográficamente, siendo los tres grupos más grandes el impacto de los macrodatos en el ciclo de políticas, la toma de decisiones basada en datos y la productividad. A través de la codificación cualitativa de la literatura, nuestro estudio destaca el núcleo de las discusiones y propone una agenda de investigación para estudios posteriores. Big data and data analytics have been seen as augmenting knowledge, ultimately leading to better decision-making. Arguments such as that the broad-based use of big data and data analytics will lead to the end-of-theory speak volumes about our expectations of big data and data analytics technologies' transformative power. While industry has been leading the way to test big data and analytics, public actors have been slower to engage (Poel et al., 2018), despite an equal opportunity for big data and data analytics to augment the public policy process. Utilizing big data and data analytics has become a near necessity due to our increasing capability for creating and collecting data at an extraordinary rate. The terms "big data" and "data analytics" have been among the buzzwords of recent years, leading to an upsurge in research, industry, and government applications (Zhou et al., 2014). The increased interest in big data in public policy can be seen in the scientific literature Figure 1, highlighting the increase in big data and analytics related literature. We also see public organizations increasingly engaging with big data analytics to solve challenges like the sustainability crisis and pandemics.1 Scholarly discourse has highlighted case studies and narratives on implementing big data and data analytics in the policy process. However, the literature lacks a systematic view of the current state of big data and data analytics in public policy, and there are identifiable research gaps (Desouza & Jacob, 2017). RQ1. What are the thematic communities of big data and data analytics literature concerning public policy-making? RQ2. What are the research questions emerging under each of the thematic research communities? Our study adopted a mixed-method systematic literature review approach based on a robust empirical bibliometric analysis followed by a qualitative analysis of the core documents to answer these questions. Using a well-established bibliometric method, bibliographic coupling, we identified thematic differences within the literature, and here, we highlight points of departure from the extant literature. The bibliometric analysis was, in turn, used as a basis for the qualitative analysis of the core literature, which is used to propose a research agenda. We find nine contemporary research communities addressing different aspects of big data and data analytics in public policy. While these communities have significant overlap, our analysis identifies them drawing from different theoretical foundations. Moreover, we demonstrate three larger research strands taking different vantage points, namely building strategic capability, data-based decision-making and productivity increases. Finally, our work proposes a research agenda focusing on the role of strategic capability, data-based decision-making, how to address expectations for better services while simultaneously increasing productivity and how to leverage policy analytics and empiricism. Our results offer scholars in public policy a vantage point to the theoretical foundations of research in big data and data analytics in public policy-making. We also draw from the identified communities to highlight emerging research themes that can guide research forward. For policymakers, our results highlight the on-going scholarly debate that focuses on addressing critical issues in the adoption of big data in public policy-making, namely capability building and the extent of data-based decision-making. This article will proceed as follows: next, we review the central elements of big data in policy-making. This is followed by a description of the data and our mixed-method approach. Finally, the empirical results are described and followed by a discussion to make sense of the research themes emerging from the analysis. "Big data" is a general term used for the process of gathering massive amounts of data from different sources. Sources can include human-input data but also includes data from sensors or different types of monitoring systems that create process data while running. It is clear that we are accumulating data at a never before seen rate. Already, in 2014, the pace was staggering, with 90% of the world's data being collected during the prior 2 years and 2.5 quintillion bytes of data added each day (Kim et al., 2014). Having access to massive amounts of data has enabled significant innovation in both the public and private domains. Looking at companies like Google and Amazon, with their innovation of new services for consumers, or at the recent ability for doctors to detect cancer cells more precisely thanks to massive training data about what a cancerous cell is, we can see that we are very much on the cusp of creating a broad utility of big data and analytics. This has been seen as a shift in the Industrial Revolution's magnitude (Richards & King, 2014) and has been widely hyped in business (Margetts & Sutcliffe, 2013). That said, public policy is not at the forefront of the use of big data and data analytics in decision making (Kaski et al., 2019; Poel et al., 2018). This nonadoption is due to multiple factors limiting these technologies' utility (Malomo & Sena, 2017). The ever-increasing amount of data offers possibilities for discovering new relationships and inferencing a multitude of problems. However, this comes with new challenges involving reproducibility, complexity, security, and risks to privacy and a need for new technology and human skills. This is very much the case in public policy, where we need to clearly identify where big data can add value in an ethical and trustworthy manner. In a review, Giest (2017) highlighted three underlying factors to consider. First, institutional capacities have a significant role in the use of big data in public policy, producing solutions that can enable users to easily interact with data while also taking into account the siloed data structures in the public domain. However, we know from previous research that siloed structures are an important limiting factor for public policy utilization of big data (Malomo & Sena, 2017). Second, hand-in-hand with big data comes the broader digitalization of public services. Digitalization allows for mediums to interact with big data but also enables the creation of new data. There is, however, evidence that digitalization changes the interactions between citizens and public officials and requires new skills from both parties. Third, big data information will have an impact on the policy cycle. Studies have found that there has been limited progress in taking advantage of big data and analytics (Poel et al., 2018) because it requires a significant change in the policy cycle (Höchtl et al., 2016). Giest (2017) highlights two issues, the substantive role and the procedural role of big data in policy instruments. Procedural activities focus on regulatory activities, such as enabling open data, while substantive actions relate to collecting data for enhancing, for example, evidence-based policy making. Capacities, digitalization, and the role of big data in the (substantive and procedural) policy cycle are core to digital-era governance and evidence-based policy making. In this, it is important to note that policy-makers are not a homogeneous group, and policy cycles vary. Thus, the objectives of analytics throughout the policy cycle vary significantly (Daniell et al., 2016) whether or not we approach the policy cycle as separate discrete stages (Jann & Wegrich, 2007), and it has been shown that big data analytics, when used more in some policy stages than in others, notably improved government transparency, policy evaluation, foresight, and agenda setting (Poel et al., 2018). This should be reflected against findings that data analytics have been politically significant in all policy cycle stages (Van der Voort et al., 2019). To overcome the challenges, Poel et al. (2015) highlighted multiple topics that must be addressed to enable capacity building, digitalization, and data integration into the policy cycle. These are (1) a skills gap, (2) reduced transparency due to data analytics, (3) sources and tools, (4) standardization of methods and tools, (5) linking of policy experiments with impact assessments, and (6) enabling policy-makers to be informed about the tools that are developed and piloted. The highlighted themes give context to the issue of big data in policy. While we see the significant impacts being created by the use of big data in policy making, along with the subsequent adaptation of data analytics, we need to better explain and make transparent the utility and complementarity of big data-driven analyses for the policy cycle (Vydra & Klievink, 2019). The challenge highlighted by Poel et al. (2015) and Giest et al. (2017), is also reflected in Pencheva et al. (2020) and Ingram (2019). Both note that big data in public policy has focused more on the "techno-rational factors," dismissing the importance of interaction with the policy process. We know that technology adoption is dependent on the perceived usefulness by the user (Venkatesh & Davis, 2000) and that there is scepticism towards the use of big data and data analytics is public policy-making (Guenduez et al., 2020). This can be the result of a mismatch in practise and expectations. Durrant et al. (2018) show how there is a aspirational motivation to the use of big data and data analytics, not reflected by its everyday utility. While we know that by employing data drive approaches, there is significant potential for anticipatory governance, interaction among stakeholders is the key to draw value from big data and analytics (Maffei et al., 2020; Starke & Lünich, 2020). While the increased stakeholder involvement does not protect from big data and data analytics policy-making creating hard to detect inequalities (Giest & Samuels, 2020; van Veenstra et al., in press). However, engaging with a large pool of stakeholders in the public policy-making process will increase the complexities of adopting big data and data analytics (Janssen et al., 2017). In addition to stakeholder interaction, the ability to build capacity and also evaluate deficiencies is important (Okuyucu & Yavuz, 2020). Building capacity should not be merely seen as the technical capacity, while that is also important (Poel et al., 2018), but a more holistic capability to integrate big data and analytics into the policy cycle (Höchtl et al., 2016). Policy-making organizations, while being exceptionally technically capable, can be in a situation where the benefits from big data and data analytics remain small due to "applications" not fitting "their organizations and main statutory tasks" (Klievink et al., 2017). This to say that when we talk about big data and data analytics capabilities in public policy-making, the literature focuses on technical issues but also on the ability of big data and data analytics to produce policy-relevant applications. While we see an increasing and diverse set of research addressing different challenges of big data and data analytics, the current body of literature lacks holistic research agendas (Desouza & Jacob, 2017) addressing the issues highlighted from practice by Giest (2017) and Poel et al. (2015). While we can note emerging fields such as policy analytics (De Marchi et al., 2016; Tsoukias et al., 2013), there is a need to better understand the theoretical grounding and research gap of big data and data analytics in public policy-making. This study's methodological approach was based on a mixed method of quantitative and qualitative analyses of the bibliometric data and the publication's content. The selected four-step mixed-methods approach, described below, enables a holistic approach to comprehending the current state-of-the-art and allows us to propose an agenda for going forward. The first phase focused on retrieving the sample of relevant articles and their bibliometric data for analysis. The second phase involved the bibliometric analysis of the retrieved data, which was performed by analysing descriptive statistics, bibliographical coupling, network analytics, and community detection. By gaining a comprehensive view of the more extensive body of literature, we could implement more filtering process based on eigenvector centrality to get a shortlist of papers for the next phase. The third phase, qualitative analysis, continued the process with an in-depth review and coding of the articles' full text. Finally, in the fourth phase, Synthesis, we draw insights from the MAXQDA coding analysis and reporting. This four-phase process is shown in Figure 2. The data used in this study were retrieved from the Scopus database. Scopus is Elsevier's abstract and citation database that has over 1.7 billion cited references dating back to 1970. A central aspect of the quality of the results is that the query used to search for relevant articles was correctly designed. The study focuses on public policy-making and big data and data analytics. The study's scope is relatively narrow, focusing solely on publications that address policy-making and the policy process. The decision of the scope excludes articled that focus on, for example, big data or data analytics, but lack the specific aspect of policy-making. This was the key inclusion criteria of articles into the data set. To focus on this specific scope, we used an iterative approach where multiple search strings were tested, and after each search, the abstracts of the 10 most cited articles and the 10 most recent articles were reviewed to understand if the query results reflected the objectives of the study. In practice, the process started with a seed query of "big data" or "data analytics" and "public policy." The query results were reviewed to estimate which articles focused on big data or data analytics and policy-making. These articles were reviewed to see if new terms emerged through the titles, abstracts, and keywords that needed to be included in the analysis. The process adjusted based on a subjective evaluation of the number of false-positives in the 10 most recent and 10 most cited publications and the number of articles retrieved. This method of short-listing the important literature is known as the snowball method, and the process includes consulting the bibliographies of the key documents to find other relevant titles in the subject (Jalali & Wohlin, 2012). After multiple tests of a comprehensive query that also limited the number of false-positives, we downloaded the metadata for 538 documents. These documents were retrieved using the query "public policy," "policy analysis," "policy making," or "public administration," with the terms "big data," "data analytics," or "automated decision-making" in the title, abstract, or keywords of the document. To analyze the literature, we used the well-established bibliometric method of bibliographical coupling. Bibliographical coupling allows for analysis of the publications' shared intellectual background (Kessler, 1963), highlighting contemporary research (Youtie et al., 2013). It is an approach to analyzing the shared theoretical background of scientific publications where the link between documents is calculated by the number of references the two documents share. Kessler (1963) elaborates, "A single item of reference shared by two documents is defined as a unit of coupling between them," and if multiple references are shared, the weight of the coupling increases. Bibliographical coupling is able to highlight hot topics (Glanzel & Czerwon, 1996) and links documents with a similar research focus (Jarneving, 2007), ultimately creating a "contemporaneous representation of knowledge" (Youtie et al., 2013). This approach has been used in several research papers to form the basis for research agenda building (Suominen et al., 2019; Yuan et al., 2015). Using the retrieved publication metadata, the VOSviewer tool (van Eck & Waltman, 2009) was selected to calculate bibliographical coupling weights for all the documents in our data set. VOSviewer is a free tool used for bibliometrics and was selected due to the exports available in the software allowing for deeper network analysis in Gephi. The SCOPUS data export was used as an input to the VOSviewer. During the analysis process, we selected documents as the level of analysis, minimum number of citations for a document was set to zero and the full set was selected for the analysis. The full counting method, which assigns each researcher with full credit of one publication rather than a fractional share per the number of authors, was used for the calculation method. Finally, we accepted VOSviewer default to keep the most extensive set of related items, which limited the analysis to the largest, by created by the bibliographical coupling analysis. This limited the analysis to documents. 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ObjectiveTo determine how often clinical research from doctoral degree programmes is unpublished and to see if one can characterise differences between those researchers who do or do not publish their...

The confluence of science, technology, and medicine in our dynamic digital era has spawned new data applications to develop prescriptive analytics, to improve healthcare personalization and precision medicine, and to automate the reporting of health data for clinical decisions.1 Data science in health care has seen recent and rapid progress along 3 paths: (1) through big data via the aggregation of large and complex data sets including electronic medical records, social media, genomic databases, and digitized physiological data from wireless mobile health devices2; (2) through new open-access initiatives that seek to leverage the availability of clinical trial, research, and citizen science data sources for data sharing3; and (3) in analytic techniques particularly for big data, including machine learning and artificial intelligence that may enhance the analyses of both structured and unstructured data.4 As new data sets are created, analyzed, and become increasingly available, several key questions emerge including the following: What is the quality of unstructured data generation? Will the use of nonstandardized methods in data processing with traditional software and hardware lead to data fragmentation and analyses that are nonreproducible? Will healthcare systems incorporate and use big data especially from new publically and patient-generated sources? How will physicians and researchers learn from new open-sourced data and big-data analytics? And ultimately, How can they acquire the skills to create a knowledge translation in data sciences?5 Practicing in an era of continuous payment reform and decline in research funding, early career investigators are challenged to keep up with the accelerating pace of change in medicine, all while being expected to provide meaningful contributions through productive clinical, educational, and research experiences.6 In this perspective, we aim to highlight how data science can catalyze professional advancement and discuss the implications of big data, open access, …

Purpose: The objective of the article is to present the results of the study pertaining to the impact of competences of small enterprises' managers upon the enterprises' performance. Design/Methodology/Approach: The review of available literature as well as empirical studies were performed in order to arrive at the objective. This enabled an in-depth examination of the subject matter. The review of literature enabled the methodology of the study to be formed. The survey approach with the use of a questionnaire was applied in data collection. The impact of the managers' competences upon the performance of the enterprises was examined by means of structural equation modeling. Findings: The study expands knowledge in the field of small enterprises management. The article revealed that the performance of such enterprises is affected by the competences of the management. Based upon the review of literature, three types of competences were distinguished. A list of competences, which takes the specific character of small businesses into consideration, was developed for each of the types. The empirical study revealed that managerial competences directly affect the performance of the enterprises. Such competences are reinforced by specialized competences as well as personal and social competences. Practical Implications: The results of the study may offer valuable information for small enterprise owners. They may prove helpful in developing competence profiles and strategies regarding the improvement of managers' competences. They may also be of use to consultants offering trainings and advisory services for small businesses. The results of the study are compelling and encourage further research. Originality/Value: The subject matter seems vital because insufficient competence level of the management is believed to be one of the weaknesses of small enterprises. Usually, such businesses are analyzed in relation to large enterprises or the entire MSME sector. The current study pertains exclusively to small enterprises, which have the lowest development capacity in the sector. Studies prove that the enhancement of the management's competences may contribute to the improvement of business performance.

The study of the problem of the competency-based approach to the training of specialists, including for law enforcement agencies, has recently been of interest to scientists and practitioners. Since one of the important types of activity is organizational and managerial activity, the criterion of professionalism of its subject and the purpose of its professional development is precisely organizational and managerial competence. However, the problem of the formation and development of organizational and managerial competence of the investigator has not been studied at the level of directed scientific work; first of all, the essence of organizational and managerial competence in the psychological structure of the investigator’s professional activity and its fundamental competences have not been determined. That is why the purpose of the study was to determine the psychological content of organizational and managerial activities in the process of pre-trial investigation of offenses and to determine the key organizational and managerial competencies of the investigator as the basis for the formation of professionally important personality traits in the direction of improving his organizational and managerial activities. Methodological basis of the study is the theory of management psychology and the psychology of investigative activity, the concept of the formation of professional competence. According to the purpose and for solving problems the following methods were used: analysis of scientific researches in psychology of investigative activity, psychology of management and management; structural and system analysis to ensure the complexity of the substantiation of the concept of development of organizational and managerial competence of the investigator; formalization, generalization – to justify the conceptual apparatus of research and formulation of conclusions. Scientific novelty. The modern content of organizational and managerial competence in the professional activities of the investigator based on police management is revealed, its specific psychological features are determined. The notion of «investigator-manager» is formulated as a professional in the field of jurisprudence, who knows the basics of management, has the skills and organization of professional activities during the pre-trial investigation, the business leader of the SOG and other groups of participants in the pre-trial investigation. Conclusions. Organizational and managerial activities of the investigator are ensured by both the general parameters of the personality’s professionalism, and the individual organizational and leadership abilities of the investigator, a high level of self-organization, a system of mental properties and qualities that characterize the emotional-volitional stability of the subject of investigation and a creative approach to solving various professional tasks. Therefore, the investigator’s effective organizational and managerial activity is organizational and managerial competence, which on the one hand consists of a number of organizational and managerial competencies, and on the other, acts as a high-quality personal characteristic, which includes the ability and ability to organize and organize other people for joint activities, influence the processes of this activity, make organizational and managerial decisions regarding pre-trial been concerned. Keywords: investigator; pre-trial investigation; organization; planning; organizational and managerial competence.

Objective. The purpose of this article is to provide a theoretical justification and practical analysis of the possibilities of using artificial intelligence tools to modernise the methods of assessing the creditworthiness of enterprises. The article also examines the impact of these technologies on the formation of effective financial strategies in the activities of financial and credit institutions. Methodology. The methodological basis of the study includes a set of general scientific and special methods that provide a comprehensive analysis of the use of artificial intelligence to assess the creditworthiness of enterprises. The use of these methods allows us to scientifically substantiate the results obtained and develop practical recommendations for financial institutions to optimise credit analysis. Results. The study has shown that the use of artificial intelligence significantly increases the likelihood of an accurate assessment of the creditworthiness of enterprises and eliminates the impact of human error. Traditional methods of credit analysis have significant limitations, while artificial intelligence enables fast processing of large amounts of data and automated decision-making. The most promising technologies in this area are machine learning, neural networks, and Big Data, which help improve financial risk assessment. At the same time, the report highlights challenges, including the high cost of implementation, the need for specialist training, and cybersecurity risks. An analysis of the regulatory framework has shown that legislation needs to be adapted to digital technologies, especially in the area of liability for automated solutions. Artificial intelligence is already being actively used in the banking, insurance and financial technology sectors, helping to reduce costs and improve risk management. The use of artificial intelligence can significantly reduce the number of defaults and allow for the creation of more personalised financial products. Thus, the use of artificial intelligence opens up new horizons for financial strategies that meet modern economic challenges. Scientific novelty. This study is a comprehensive analysis of the possibilities and implications of using artificial intelligence in assessing the creditworthiness of enterprises, taking into account current trends in the digitalisation of the financial sector. The main challenges and benefits of introducing artificial intelligence in financial institutions are systematised, and a comparative analysis of traditional and modern methods of credit analysis is carried out. The study expands the scientific understanding of the application of machine learning algorithms and neural networks in credit scoring, which allows for the improvement of financial strategies and risk management. Practical significance. The study can be useful for financial institutions in improving credit assessment and risk management methods. The findings will contribute to the development of effective machine learning algorithms that will improve the accuracy of credit analysis and automate decision-making. The use of artificial intelligence helps to optimise financial processes, reduce credit risks and improve the quality of banking and insurance services.

This article provides a detailed analysis of the process of developing digital competence among management undergraduates as a key element of modern professional training. The main components of digital competence are systematized, including informational, technical, communicative, analytical, and creative skills, as well as critical thinking and innovation capacity. International standards of digital competence (DigComp) and national educational standards of Ukraine are examined, which define the essential knowledge and skills required for future managers in the fields of digital technologies, data analytics, managerial decision-making, and information security. The study analyzes modern information technologies applied in the educational process, including learning management systems (LMS), online simulators and business games, analytical platforms, cloud services, and collaborative tools for team-based work. Methods for developing and assessing digital competence are identified, such as testing, practical assignments, project-based activities, portfolio creation, and collaborative assessment, which allow comprehensive evaluation of students’ skills and planning for their further development. Particular attention is given to the prospects of integrating modern technologies into curricula, including the use of artificial intelligence, machine learning, virtual and augmented reality, which facilitate personalized learning, modeling of complex managerial situations, and practical skill development. The advantages of actively using digital platforms for distance and hybrid learning, big data work, analytics and visualization of results, as well as development of digital communication and team collaboration, are analyzed. It is concluded that the comprehensive use of information technologies and modern pedagogical methods effectively fosters digital competence of future managers, enhances their professional readiness, analytical and communication skills, creativity, critical thinking, and adaptability to a dynamic digital environment. Such an approach ensures the preparation of competitive specialists capable of acting effectively in the digital economy and global labor market.

The Job I Never Wanted Was Exactly What I Needed Stephanie G. Adams (bio) In march 2011, i accepted the offer to become the department head of the Engineering Education Department at Virginia Tech. In the history of the department, I would be the second, full-time department head and the first, permanent, woman of color. The fact that I was the first, permanent, woman of color department head should come as no surprise given the demographics of the engineering field. In 2017, there were 143 African American women engineering professors. This represents 0.5 percent of the 27,372 tenured/tenure-track faculty in engineering. Over the last forty-plus years, the percentage of underrepresented minority faculty in engineering has increased and according to the American Society for Engineering Education (ASEE) "By the Numbers," we make up 3.1 percent of all engineering faculty. Diversity and broadening participation in STEM have been a national priority for over forty years. Industry and academic leaders recognize that diversity, in all forms, is crucial to innovation and the development of new ideas. The presence of different perspectives and experiences improves productivity, creativity, and thus innovation. With a start date of August 10, I had a lot to do in five months: find a place to live; pack and move; close out my life at Virginia Commonwealth University (VCU); and most importantly, figure out what exactly a department head was supposed to do, as this was not a job I had ever hoped to have. I had been an assistant and associate dean at two institutions with my eye on becoming a dean. I had studied and interacted with deans for many years, and I had a strategy for what I would do when I finally became a dean. After a couple of unsuccessful attempts to become a dean and with the strong urging of a colleague, I applied for this position. Before I dive into the experience [End Page 196] of being the department head, I feel it is important to start with the preparation for the interview. I researched the department from top to bottom; I combed the website; I talked to some of the faculty in the department I had come to know through professional societies; I studied similar departments around the country; and I talked with friends who were or had been department heads/chairs. When I went for the interview, I had a pretty good handle on the department, the successes it had experienced, the challenges facing it, and the resources (people, money, space, and time) available to move it forward. The department was founded in 1967 as the Engineering Fundamentals Division within the College of Engineering. The objective was to provide incoming first-year students a better understanding of engineering. The unit was viewed as a service division in the college whose primary mission was teaching. The majority of the faculty had MS degrees and tenure was based on good teaching and conference presentations. In 2004, the division was renamed the Department of Engineering Education and a new doctoral program in engineering education was launched. In addition to the launch of the graduate program, an expectation was set that the department would shift its identity from a teaching-focused department to a research-focused department. A new type of faculty member was hired with a different set of expectations. Faculty were expected to have a PhD, conduct research on par with traditional engineering departments, develop and teach graduate level courses, and advise graduate students. When I arrived, the unit was operating structurally as a department with teaching faculty, research faculty, and a new doctoral program. But the shift in focus created a major challenge; one department with two disconnected camps. Our collective task was to provide a foundation from which to begin our work toward one unified department. As I began to understand the department, I came to realize that the decision to add a graduate program and attendant research expectations had been a topdown one, and that not all in the department bought into or understood how the new graduate program would or should be integrated. In my preparation stages, I also asked for the...

Thailand’s transition to high middle-income country status has been accompanied by demographic changes and associated shifts in the nation’s public health challenges. These changes have necessitated a significant shift in public health focus from the treatment of infectious diseases to the more expensive and protracted management of non-communicable diseases (NCDs) in older adults.In 2010, in response to this shift in focus, the University of Michigan and colleagues at the Praboromarajchanok Institute for Health Workforce Development in Thailand began work on a broad-based multi-institutional programme for NCD research capacity-building in Thailand.To begin to build a base of intervention research we paired our programme’s funded Thai postdoctoral fellows with United States mentors who have strong programmes of intervention research. One direct impact of the programme was the development of research ‘hubs’ focused upon similar areas of investigative focus such as self-management of cancer symptoms, self-management of HIV/AIDS and health technology information applications for use in community settings. Within these hubs, interventions with proven efficacy in the United States were used as a foundation for culturally relevant interventions in Thailand. The programme also aimed to develop the research support structures necessary within departments and colleges for grant writing and management, dissemination of new knowledge, and ethical conduct of human subject research.In an effort to capitalise on large national health datasets and big data now available in Thailand, several of the programme’s postdoctoral fellows began projects that use data science methods to mine this asset. The investigators involved in these ground-breaking projects form the core of a network of research hubs that will be able to capitalise on the availability of lifespan health data from across Thailand and provide a robust working foundation for expansion of research using data science approaches.Going forward, it is vitally important to leverage this groundwork in order to continue fostering rapid growth in NCD research and training as well as to capitalise upon these early gains to create a sustaining influence for Thailand to lead in NCD research, improve the health of its citizens, and provide ongoing leadership in Southeast Asia.

Обоснована актуальность и перспективность исследования развития региональных систем на основе концепции ключевых компетенций. Представлена теоретико-методологическая база исследования ключевых компетенций региона. В качестве факторов, влияющих на формирование ключевых компетенций региона, обозначены процессы глобализации, цифровизации экономики и человеческий капитал. Утверждается, что конкурентоспособность региона в глобальной экономике определяется его способностью привлекать инвестиции, инновации, человеческий капитал и другие ресурсы на основе компетенций субъектов территории. Предложен макет матрицы ключевых компетенций субъектов региона: власть, бизнес, наука, как основных субъектов, определяющих возможности инновационного развития территории. Introduction. Modern challenges of Industry 4.0, digitalization of socio-economic processes, and global competition for all types of resources determine the relevance of rethinking theoretical and methodological issues of spatial development of the national economy. The aim of the research is a theoretical and methodological substantiation of the possibility of using the key competencies concept when researching the regional systems. The region is considered as a self-sufficient, self-forming, and self-developing system that has its own resources and socio-economic benefits. Moreover, it has the ability to independently form key combinations of resources that is, the region’s core competencies. The region’s core competencies are understood as the ability of the subjects of a region to establish conditions for sustainable economic development of the territory. Results. The region carries out activities in various areas of society in the face of state and municipal authorities, business and science, which makes it possible to consider the core competencies of these subjects, taking into account the criteria of universality and functionality. Universal competences are identified as following: 1) managerial competencies - the ability to organize the work of a subject (institution), the ability to form teams for solving professional tasks; 2) communicative competences - the ability to develop communications in the field of professional activity, taking into account multiculturalism and multilingual territory and markets; 3) project competences - the ability to introduce and adapt innovations in a certain field of activity; 4) digital competencies - the ability to verify and structure information obtained from various sources. As a working tool for the study of region’s core competences, a matrix of competencies was proposed. It reflects both the universal abilities of government, business, science, and the necessary functional skills. Conclusion. The analysis of theoretical concepts of foreign and domestic scientists revealed the prospects for the study of the regional systems based on the concept of core competencies. The region’s core competencies can be represented with the competencies of its subjects, including management institutions (government), business, and science. The core competences of the subjects are their abilities to provide facilities for the development of a regional socio-economic system due to the ability to develop communications and attract investment, and the ability to form teams for the development of innovations.

The relevance of project-based activities research and its perspectives development with the aim of students’ professional orientation is caused by caused by the following problems in implementing Federal state educational standards for schools: students ' readiness to choose training profiles and ways to continue their education formation under conditions of municipal educational environment integration ensuring students ' education quality by their project-based activities personalizing; improving the municipal schoolchildren guidance system management quality using information technologies and mathematical models. The work is directed towards a comprehensive solution of choosing the type of project-based activity problem depending on students’ personal characteristics. This research contributes to methodology and methods of making pedagogical decisions about the choice of the type of school students project-based activity. To solve the research problem, psychological methods (testing, surveys) were used as well as pedagogical approaches, methods of statistical data processing, Saati method of hierarchies analyzing, and information technology tools. The results of the study are the basis for developing information and analytical systems for the organization of secondary schools’ students project-based activities as well as for training teachers of the region Kemerovskaya oblast - Kuzbass for the optimal implementation of this type of activity.

The relevance of the article. The article delves into the history and cultural significance of semi-public diaries, created with the intention of being read, and compares them to modern social networks. The topic is particularly relevant taking into account the growing popularity of social networks and the transformation of traditional cultural practices of personal writing, self-reflection and self-expression. The purpose of the article. The purpose is to trace the history of semi-public diaries and compare their accounting-and-reflection practices with those found in social networks personal profiles, focusing on the interplay between public and private domains. The methodology. The article utilizes a comparative analysis of diaries and social networks, examining their historical evolution and contemporary practices, and draws on existing research from cultural studies, history, and media studies. The results. The study reveals that diaries have long been used to share individual reflections with others, making their personal writing practices similar to those of modern social network profiles. The study discovered that pre-internet era diaries and contemporary social networks have two similar intentions for personal writing: firstly, to document and reflect on personal experiences for private purposes, such as for self-reflection, life-tracking and identity formation, and secondly, to share personal ideas and experiences with others. This duality generates tension between the public and private aspects of personal writing. The scientific novelty. The study offers a fresh perspective on the role of diaries in the cultural practice of personal writing by examining their similarities with modern social networks and emphasizing the interaction between private and public domains in documenting and reflecting on personal experiences. The practical significance. Understanding the historical and cultural context of diaries and social networks can help individuals better appreciate the functions and potential benefits of these mediums for personal reflection and communication. The findings of this study can also inform future research into the role of personal writing in identity formation and the ways in which technology is transforming the nature of personal writing. The conclusions. This study demonstrates the enduring relevance of accounting-and-reflection practices of personal writing in both diaries and modern social networks. Additional comparative research is required to examine the similarities and differences in topics and styles of personal writing across diaries and social networks, as well as the balance between public and private domains when using social networks for self-expression and identity formation.

BackgroundWhile Africa accounts for a significant proportion of world population, and disease and injury burden, it produces less than 1% of the total research output within emergency care. Emergency care research capacity in Africa may be expanded through the development of doctoral programmes that aim to upskill the PhD student into an independent scholar, through dedicated support and structured learning. This study therefore aims to identify the nature of the problem of doctoral education in Africa, thereby informing a general needs assessment within the context of academic emergency medicine.MethodsA scoping review, utilising an a priori, piloted search strategy was conducted (Medline via PubMed and Scopus) to identify literature published between 2011 and 2021 related to African emergency medicine doctoral education. Failing that, an expanded search was planned that focused on doctoral education within health sciences more broadly. Titles, abstracts, and full texts were screened for inclusion in duplicate, and extracted by the principal author. The search was rerun in September 2022.ResultsNo articles that focused on emergency medicine/care were found. Following the expanded search, a total of 235 articles were identified, and 27 articles were included. Major domains identified in the literature included specific barriers to PhD success, supervision practices, transformation, collaborative learning, and research capacity improvement.ConclusionsAfrican doctoral students are hindered by internal academic factors such as limited supervision and external factors such as poor infrastructure e.g. internet connectivity. While not always feasible, institutions should offer environments that are conducive to meaningful learning. In addition, doctoral programmes should adopt and enforce gender policies to help alleviate the gender differences noted in PhD completion rates and research publication outputs. Interdisciplinary collaborations are potential mechanisms to develop well-rounded and independent graduates. Post-graduate and doctoral supervision experience should be a recognised promotion criterion to assist with clinician researcher career opportunities and motivation. There may be little value in attempting to replicate the programmatic and supervision practices of high-income countries. African doctoral programmes should rather focus on creating contextual and sustainable ways of delivering excellent doctoral education.

Mathematics education researchers (MERs) use practices unique to the mathematics education discipline to conduct their work. MERs’ practices, i.e., ways of being, interacting, and operating, define the field of mathematics education, are initially learned in doctoral preparation programs, and are encouraged and sanctioned by conferences and publications. Disciplinary practices facilitate MERs’ interactions within mathematics education. When working in interdisciplinary groups, differences in disciplinary ways of being, interacting, and operating can create challenges with completing research and other work. Since MERs’ engagement in interdisciplinary collaborations is encouraged and can result in products contributing to the evolution of the mathematics education discipline, it is important to explore what practices MERs use in interdisciplinary collaborations. We interviewed four MERs who led international interdisciplinary collaborations and used qualitative content analysis to create descriptions of practices described by MERs in their collaborations. Five practices were common between the MERs in interdisciplinary collaborations. MERs conducted interdisciplinary work by using practices that allowed them to situate themselves and others in the group (i.e., being practices), develop ideas (i.e., interacting practices), work towards common goals, and use structures to get the work done (i.e., operating practices). We argue that MERs developed new practices to position themselves and others, interact with practitioners from other disciplines, and get interdisciplinary work done. This study contributes to the evolution of the mathematics education discipline by offering five practices that can orient MERs to conducting interdisciplinary work and discussing how MERs experience interdisciplinary collaborations beyond providing mathematics education expertise.

Big Data Analytics (BDA) solutions are increasingly applied in marketing, with the aim of transforming data available online to market information and competitive intelligence. When businesses encounter risky situations (calculated risks) complex models using BDA are often the go-to solution. However, there is a still a debate if BDA is the best solution in uncertain situations when unexpected phenomena arise. The present study begins to fill this gap using the Colombian context as evidence and a Fuzzy-Set Qualitative Comparative Analysis method (fs-QCA). The focus shifts from a single cause leading to performance to an analysis of different associations of conditions. There are three major findings-first, in a risky environment, BDA is not a unique condition; a combination of data maturity levels, data skills and a data-driven mindset and culture are necessary to obtain good business performance. Second, even in the era of Big Data, large companies still use simple rules (heuristics) to make decisions which improve their performance. Finally, the environmental state (uncertain, certain, risky) is a moderating factor in the choice between BDA or Heuristics. The results indicate marketing managers should use a multi-model approach incorporating both BDA and heuristics.