Search results

Education policies in the United States reinforce social stratification by prioritizing and normalizing middle-class whiteness in schools (Leonardo, 2007; Picower, 2009). The teacher labor market has also become more feminized, making white middle-class women paragons of exemplary educators (Rury, 1989; Tolley & Beadie, 2006). These sociopolitical and historical factors continue to play out in the current U.S. education workforce where 80% teachers are white and 76% of teachers are female (Hussar et al., 2020). Meanwhile, student demographics are shifting with students of color comprising over 50% of the public student population (de Brey et al., 2019). Diversifying the educator pipeline is a well-documented strategy to improve educational outcomes for all students, specifically students of color, and to achieve greater equity and inclusion in public education. However, the retention and promotion of educators of color remains a critical and complex issue.

Thus, looking at the intersection of race and gender in the education workplace, the purpose of this chapter is to highlight the experiences and expertise of women K-12 educators of color to identify best practices for career development. Applying Psychology of Working Theory (PWT) and utilizing modified meta-synthesis methodology, the chapter highlights the experiences of Black, Latinx, Asian American, and Indigenous/Native American women K-12 principals and superintendents to (1) thematize and conceptualize how women of color define their work in education spaces through a PWT lens and (2) understand how PWT themes can illuminate ways to build more diverse and inclusive career pathways for women of color leaders.

This chapter offers a comprehensive review the literature on authentic leadership (AL). The authors employ a bibliometric approach to identify, classify, visualise and synthesise relevant scholarly publications and the work of a core group of interdisciplinary scholars who are key contributors to the research on AL. They review 264 journal articles, adopting a clustering technique to assess the central themes of AL scholarship. They identify five distinct thematic clusters: authenticity in the context of leadership; structure of AL; social perspectives on AL; dynamism of AL; and value perceptions of AL. Velt and Sinkovics assert that these clusters will help scholars of AL to understand the dominant streams in the literature and provide a foundation for future research.

This research aims to analyze how companies in the industry act as orchestrators and manage participants in their networks. The service industry has received little attention from scholars in this field, and this research gap was investigated in this study. Qualitative case study research with an exploratory approach is applied in this research so that in-depth insights can be obtained. Mamikos.com, a platform for room rent in Indonesia, was used as the research subject. Mamikos does not only facilitate tenants to find rooms to live in but also manages how landlords can serve tenants better and help tenants understand what the landlords want. In other words, Mamikos seeks to smoothen the value stream from landlords to tenants and vice versa. This research has implications that the value orchestrator must be able to facilitate the parties in the ecosystem to obtain the fundamental values needed and other side values so that they are loyal to the ecosystem managed by the orchestrator.

In this chapter, rephrasing Spivak's question into ‘can subaltern children speak?’, I reorient the research on China's gigantic population of children and youths in rural migrant families towards a critical interpretative approach. Based on life history and longitudinal ethnographic interview gathered with three cases, I unpack the multiple meanings migrants' children attach to mobility in their childhood experiences. First, despite emotional difficulties, children see their parents' out-migration more as a ‘mobility imperative’ than their abandonment of parental responsibilities, which should be contextualized in China's long-term urban-biased social policies and the resultant development gaps in rural and urban societies. Second, the seemingly ‘unstable’ and ‘flexible’ mobility patterns observed in migrant families should be understood in relation to a long-term family social mobility strategy to promote children's educational achievement and future attainment. The combination of absent class politics in an illiberal society with an enduring ideology of education-based meritocracy in Confucianism makes this strategy a culturally legitimate channel of social struggle for recognition and respect for the subaltern. Last, children in migrant families are active contributors to their families' everyday organization amidst mobilities through sharing care and household responsibilities, and developing temporal and mobility strategies to keep alive intergenerational exchanges and family togetherness. The study uncovers coexisting resilience and vulnerabilities of migrants' children in their ‘doing class’ in contemporary China. It also contributes insights into our understanding of the diversity of childhoods in Asian societies at the intersection of familyhood, class dynamics and cultural politics.

Based on the poll’s results, the financial services sector is now on the edge of a digital revolution. Financial institutions are undergoing a period of radical change due to technological advancements in the digital sphere. The widespread use of cutting-edge digital technologies in the real world has accelerated the shift from the conventional economy to the digital economy. Total operational risk as a percentage of total bank capital was 0.65%, which is below the minimum permitted figure. This metric allows for the diversification of company risks. This chapter proposes a novel, future-oriented strategy for studying financial crises, expanding the digital transformation research agenda across disciplines. Accordingly, the authors simulated the study object’s operational risk using an optimal approach to measurements (AMA) in accordance with Basel II (Santander Bank). The study’s findings enabled the authors to determine whether or not the value was necessary for Santander Bank to bear in the next years by calculating the overall value of operational risks and evaluating the indicator’s acceptability relative to the bank’s capital.

Sustainable development calls for a larger share of intermittent renewable energy. To mitigate this intermittency, Compressed Air Energy Storage (CAES) technology was introduced. This technology can be made more sustainable by recovering the heat of the compression phase and reusing it during the discharge phase, resulting in an adiabatic CAES without the need for burning of fossil fuels. The key process parameters of CAES are temperature, pressure ratios, and the mass flow rates of air and thermal fluids. The variation in these parameters during the charge and discharge phases significantly influences the performance of CAES plants. In this chapter, the transient thermodynamic behavior of the system under various operating conditions is analyzed and the impact of heat recovery on the discharge phase energy efficiency, power generation, and CO₂ emissions is studied. Simulations are carried out over the air pressure range from 2,500 to 7,000 kPa for a 65 MW system over a five-hour discharge duration. It is also assumed that the heat loss in the air storage and the hot thermal fluid tank is insignificant and standby duration does not impact the status of the system. This result shows that the system exergy and the generated power are more sensitive to pressure change at higher pressures. This work also reveals that every 10°C increase on the temperature of the stored air can lead to a 0.83% improvement in the energy efficiency. The result of the transient thermodynamic model is used to estimate the reduction in CO₂ emissions in CAES systems. According to the obtained result, a 65 MW ACAES plant can reduce about 17,794 tons of CO₂ emission per year compared to a traditional CAES system with the same capacity.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).