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Infanticide stands out as a crime which, in England and Wales, has been marked for at least 200 years by deep-rooted continuities in its representation and treatment by both the criminal justice system and the media, despite the massive political, economic, social, legislative and cultural changes that occurred over this period. Particularly remarkable about this long-standing discourse is its routine emphasis that the guilty mother is also a victim of tragic circumstances that led to the crime and deserving of sympathetic treatment. It also invariably sets infanticide apart as a ‘special case’ which does not necessarily fit with either medical or legal definitions of diminished criminal responsibility. Perhaps surprisingly, this framing of women who commit infanticide stresses not only their ‘normality’ prior to the offence but also their ‘respectability’, a sharp contrast to the sometimes overtly misogynistic representation of other types of women offenders. This chapter argues that it is above all ‘respectability’ that profoundly shaped the cultural script relating to infanticide in England and Wales between 1800 and 2000, and that this continues to exert a powerful legacy on the relatively small number of cases that now comes before the courts in the twenty-first century.

This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy.

Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations.

Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.

While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

Current research aims to investigate the relationships between Chinese national cultural values (uncertainty avoidance (UA), power distance, masculinity (MAS), individualism (IDV) and Confucian dynamism) and accounting practices (professionalism, uniformity, conservatism and secrecy).

A sample of 842 users/preparers of financial statements participated in this cross-sectional, questionnaire-based survey from China. Covariance-based structural equation modeling (CB-SEM) was used to test the proposed relationship.

Results show that cultural values strongly impact financial reporting practices in China. Chinese society is characterized by low UA, high power distance, collectivism, future orientation (Confucianism) and masculine traits. These values show an overall preference for uniformity, conservatism and secrecy in financial reporting with weak professionalism. The findings show that Chinese society emphasizes law abidance, strict codes of conduct, written rules and regulations and respect for consistent orthodox measures.

This study provides valuable input for policymakers in developing regulations and accounting standards in the Chinese market. Understanding the relationship between cultural dimensions and accounting values helps to address societal challenges and align policies with cultural values to acquire desired financial reporting values. Global firm managers must consider cultural dimensions in accounting when entering Chinese markets or negotiating with partners from different cultures. Findings also suggest local managers gain self-awareness of their cultural biases and accounting values, enabling them to navigate businesses and society's financial reporting needs.

This study enriches the existing literature on cultural and accounting practice studies by validating the role of stakeholder and social contract theories in Gray–Hofstede’s framework and highlighting the influence of dominant cultural values on accounting values. The study provides a unique empirical analysis of the Chinese market by using a questionnaire survey and structural equation modeling (SEM). Further, it also opens avenues for future research on the relationship between cultural dimensions, accounting practices and their global impact. These findings emphasize the importance of cultural sensitivity and adaptability, especially in multicultural environments.

This paper aims to present the findings of a government-initiated project that sought to explore the possibility of incorporating cultural connections to land within the federal national accounting system using the United Nations Systems of Environmental-Economic Accounting (UN-SEEA) framework as a basis.

Adopting a critical dialogic approach and responding to the calls for critical accountants to engage with stakeholders, the authors worked with two Indigenous groups of Australia to develop a system of accounts that incorporates their cultural connections to “Country”. The two groups were clans from the Mungguy Country in the Kakadu region of Northern Territory and the Ewamian Aboriginal Corporation of Northern Queensland. Conducting two-day workshops on separate occasions with both groups, the authors attempted to meld the Indigenous worldviews with the worldviews embodied within national accounting systems and the UN-SEEA framework.

The models developed highlight significant differences between the ontological foundations of Indigenous and Western-worldviews and the authors reflect on the tensions created between these competing worldviews. The authors also offer pragmatic solutions that could be implemented by the Indigenous Traditional Owners and the government in terms of developing such an accounting system that incorporates connections to Country.

The paper contributes to providing a contemporary case study of engagement with Indigenous peoples in the co-development of a system of accounting for and by Indigenous peoples; it also contributes to the ongoing debate on bridging the divide between critique and praxis; and finally, the paper delves into an area that is largely unexplored within accounting research which is national accounting.

Spirituality is a concept that explains the spiritual dimension of human beings. Meaning of life, being together, and the bigger one's existence are the components of spirituality. In that manner, workplace spirituality is an area that endeavors to find the meaning of the work together with the community. Research reveals that workplace spirituality has several dimensions, and inner life, sense of community, and meaning at work are validly used. Though developing in the research area, misunderstandings and misuses of the phenomenon causes several dark sides. In this context, this chapter aims to reveal these in five aspects to contribute to enhancing the literature. The ambiguity in defining spirituality and religion is the first dark point in understanding workplace spirituality. Accepting spiritual executions at work as a tool for profitability is the second. The scarcity of awareness of executive leaders about workplace spirituality is the other. Considering the studies on workplace spirituality as a fad and the expected difficulties in developments about the area is the fourth. Finally, workplace spirituality-based misbehaviors at work are the last dark side, addressed in this study. In addition to the explanations in its content, the authors present a bibliometric analysis conducted by R. The chapter concludes with general evaluations and suggestions for future studies.

This paper aims to identify the income and price elasticities of demand for residential electricity in Latin America and the Caribbean (LAC) and to verify their main determinants.

Meta-analysis and meta-regression methods were applied. After collecting and filtering journal articles, the authors obtained a sample composed of 76 studies covering 1979–2020.

The results show that the LAC's income elasticity is approximately 0.20 and 0.92 for the short and long term, respectively. The LAC's price elasticity is approximately −0.37 and −0.46 for the short and long term, respectively. Furthermore, the estimates are affected by the data structure, the estimation method used and the sampling period.

The authors close a gap in the literature by analyzing the price and income elasticities of demand through meta-analysis and meta-regression.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Disciplinary school exclusion has negative consequences in terms of academic achievement, well-being, mental health, and future prospects. Permanent and temporary school exclusions rates in England are much higher than in the rest of the United Kingdom and disproportionately affect students with special needs, from care backgrounds, living in poverty, and from particular ethnic backgrounds. This chapter argues that looking at the issue of school exclusion is another way of looking at issues of inclusion and diversity in schools and that these are central concerns for initial teacher education programs. The chapter illustrates this argument by reporting some of the preliminary findings from the 4-year ESRC funded project The Political Economies of School Exclusion Across the UK (2019–2023) led by the Excluded Lives Research team at the University of Oxford. The main objective of this research has been to develop a home international multidisciplinary understanding of the landscapes of political economies and the experiences and consequences of school exclusion across the United Kingdom.