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In the new development stage of comprehensively building a socialist modern state, it is imperative to adhere to the guidance of Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era, comprehensively summarize China's practical experiences in economic development, strengthen research on capital issues, construct theories of socialist political economy with Chinese characteristics regarding capital and provide scientific theoretical guidance for further promoting the positive role of various types of capital while preventing and overcoming their negative effects, which is a major theoretical issue and a glorious task for the theoretical and economic circles in China.

From the perspective of Marx's theory on capital and historical development, modern capital represents the organizational mode of socialized mass production and market economy. It serves as both the economic foundation of bourgeois society and a tool for socialist economic development.

The market economy represents an inevitable historical stage and form of socialist economic development, necessitating the adoption of capital as an organizational form within socialist economies.

The utilization of capital to advance a socialist economy is a remarkable achievement by the CPC and Chinese people, representing a significant innovation in both theory and practice. The role of capital is inherently dual under any social condition. In the context of a socialist system, capital can play a positive role effectively, and its behavior can be guided and regulated correctly to curb its negative or even destructive impact.

This study aims to examine the impact of independent directors' ownership on corporate social responsibility (CSR) performance. In line with the stakeholder-agency paradigm's prediction, the authors propose that higher independent directors' ownership is associated with higher CSR performance. By drawing on the attention-based view, the authors further examine firm-level conditions that impact the situated attention of independent directors holding high equity ownership as they are active agents.

The authors collected data covering the years 2009–2013 for firms listed in the S&P 500 index. The authors tested the hypotheses using firm fixed-effects models.

The results show that higher independent directors' ownership is associated with higher CSR performance. Prior firm performance and available slack resources are found to have diverse impacts on the association between independent directors holding high equity ownership and CSR performance.

This study highlights the importance of examining the performance-based incentives of independent directors on firms' CSR performance. This study also provides a better understanding of factors impacting independent directors' situated attention as boundary conditions.

As emerging scholars of color with transnational backgrounds, we collectively recount our socialization experiences in US higher education institutes. We explore moments of betweenness as catalysts for envisioning a more inclusive academia that operates beyond the tokenism of diversity.

Employing betweener autoethnography (Diversi and Moreira, 2018), we inquire into the sense of impasse encountered by South Korean female emerging scholars in the field of education in becoming an outsider within the academic system.

Chronicling our shifts in perspectives of our positionality, we interweave inquiries motivating us to challenge normative pressures and map our betweener experiences onto the Wiedman and DeAngelo’s (2020) socialization model. Through this process, we wedge open in-between spaces in the socialization process that accommodate the nuanced positionality of transnational scholars.

Integrating postcolonial critiques on the Western-centric meritocratic academia, this piece sheds light on the complexity and fluidity of emerging transnational scholars’ socialization processes. The thick, nuanced description deepens the understanding of the complexity of their identity negotiation within the dominant logics of academia. Our inquiries interwoven through betweener autoethnography serve as guidance for mentoring international graduate students and transnational scholars.

The gender composition of teams remains an important yet complex element in unlocking the success of collaboration and performance in the metaverse. In this study, the authors examined the collaborations of same- and mixed-gender dyads to investigate how gender composition influences perceptions of the dyadic collaboration process and outcomes at both the individual and team levels in the metaverse.

Drawing on expectation states theory and social role theory, the authors hypothesized differences between dyads of different gender compositions. A blocked design was utilized where 432 subjects were randomly assigned to teams of different gender compositions: 101 male dyads, 59 female dyads and 56 mixed-gender dyads. Survey responses were collected after the experiment.

Multilevel multigroup analyses reveal that at the team level, male dyads took on the we-impress manifestation to increase satisfaction with the team solution. In contrast, female and mixed-gender dyads adopted the we-work-hard-on-task philosophy to increase satisfaction with the team solution. At the individual level, impression management is the key factor associated with trust in same-gender dyads but not in mixed-gender dyads.

As one of the pioneering works on gender effects in the metaverse, our findings shed light on two fronts in virtual dyadic collaborations. First, the authors offer a theoretically grounded and gendered perspective by investigating male, female and mixed-gender dyads in the metaverse. Second, the study advances team-based theory and deepens the understanding of gender effects at both the individual and team levels (multilevel) in a virtual collaboration environment.

This paper aims to investigate the relationship between corporate tax avoidance and environmental, social and governance (ESG) performance and the moderating effect of financial constraints on the relationship between corporate tax avoidance and ESG performance.

The sample consists of a global data set involving 24,259 firm-year observations from 49 countries for the years 2011–2020. Corporate ESG performance was extracted from the Thomson Reuters database. The book-tax difference model was used for measuring corporate tax avoidance, while financially constrained firms were identified using the Kaplan and Zingales (1997) index.

The results show that firms with higher tax avoidance are associated with higher ESG performance, but lower ESG performance is shown for firms with higher financial constraints. The results further indicate that the positive impact of corporate tax avoidance on ESG performance becomes weaker for firms with higher financial constraints.

The findings imply that policymakers and regulators should focus on mechanisms to promote more internal funds to assist firms in pursuing ESG-related initiatives, such as through tax incentives. Investors should understand the “smokescreen” effect of corporate tax avoidance on ESG performance, especially for firms with financial constraints.

This analysis provides international evidence on the link between tax avoidance and ESG and considers the joint effect of pressures for internal funds, through tax and financing constraints, on corporate ESG performance.

This study aims to examine whether and how corporate sustainability capability influences energy efficiency through competitive intensity and slack resource availability.

The authors applied a two-wave research design and administered a survey questionnaire to senior-level managers of 78 ISO-14001 and ISO-50001 certified manufacturing companies. The authors use a multi-method approach for data analysis. AMOS 23 software was applied for covariance-based structural equation modeling. In addition, SPSS 25 software was applied for hierarchical regression analysis to examine the causal relationships in the model.

The finding reveals that corporate sustainability capabilities, which include energy-saving opportunities, seizing energy-saving opportunities and resource reconfiguration, significantly improve firms’ energy efficiency. In addition, competitive intensity and slack resource availability positively moderated the relationship between corporate sustainability capability and energy efficiency.

To the best of the authors’ knowledge, this study is the first to examine the link between corporate sustainability capability and energy efficiency in developing countries such as Pakistan. Although the influence of various corporate sustainability capabilities on sustainable performance has been widely examined in the literature, the role of corporate sustainability capability has been limitedly explored with energy efficiency. This study extends the literature by adding to the knowledge of corporate sustainability capability that enhances boundary conditions in developing countries.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper improves the evaluation index system of green building operation effect and establishes the evaluation model of green building operation effect. It is expected to promote energy saving and emission reduction and provide a more scientific evaluation method for green building operation effect evaluation.

First, 20 key evaluation indexes are selected to establish the operation effective evaluation index system. Then, the combined weight method is proposed to determine the weight of each evaluation index. Next, the gray clustering-fuzzy comprehensive evaluation method is used to construct the green building operation effective evaluation model. Finally, the feasibility and validity of the selected model were verified by taking Shenzhen Bay One green building in Shenzhen as an example.

This paper establishes the evaluation system of green building operational effect, and evaluates green building from the angle of operational effect. Taking Shenzhen Bay One project as an example, the rationality and applicability of the model are verified.

In this paper, for the first time, relevant indexes of user experience are included in the evaluation system of green building operational effect, which makes the evaluation system more perfect. In addition, a more scientific fuzzy gray clustering method is used to evaluate the operational effect of green building, and a new evaluation model is established.

Pakistan had never been a place of serious and nuanced debate and contestation of politics of postcolonial critique, that is, the continuity of economic, political, and cultural dependency of newly independent countries (NICs) on ex-colonizers as pointed out by neocolonialism, dependency theory, and postcolonial theory, respectively. Instead, Pakistan is presented by extant liberal academic literature as a “failed nation” and a state dominated by the military and plagued by religious extremism. As opposed to this, through the literary and activists writings of Aziz-ul-Haq, this chapter will try to illustrate how cultural contestation of the nation-building project postindependence from British rule was a lot more complex and interesting in Pakistan. This was so because the nation-building project of Pakistan was, on the one hand, an amalgamation of Indo-Persian, Arab, Indian, and Western colonial and civilizational influences and, on the other hand, entailed suppression of resilient local and national cultures of its constituent nationalities developed over centuries. This was later expressed in ethno-nationalist politics. However, when it came to the politics of the marginalized in the late 1960s, there were important political, theoretical, and literary insights which caused a change in the direction of political practice in Pakistan, which paralleled the politics expressed by writers like Fanon and early Subaltern Studies influenced by the Naxal Movement in India. The contestation and confusion arising from this dialectic also entered Pakistan's literary and cultural sphere. This chapter not only tries to give a different postcolonial critique of the failure of nation-building project in Pakistan but, though at a preliminary level, is an attempt to separate the original postcolonial theory in its radical tradition from contemporary postmodern/poststructuralist postcolonial theory marked with pessimism and resignation.