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The current study investigates perceptions of street harassment from a linguistic perspective. With regard to the theory of speech acts, some may deem street remarks as compliments instead of catcalls. There is a lack of linguistic research regarding the issue conducted with a Bruneian demographic. This study recognises the difference in the use of language by men and women and aims to find whether there is a difference in their perceptions of street remarks.

A method of triangulation between questionnaire surveys and focus group interviews was carried out to actualise these aims. Thirty-two female and thirty-two male respondents from the survey were used to conclude quantitative findings, whereas three male and three female participants were recruited for the focus group interview. Data were analysed through a t-test and discourse analysis consecutively.

Quantitative data (p = 0.398) reveal that both men and women perceive street remarks almost equally as a form of street harassment. However, qualitative data reveal that male language and behaviour portray a more positive and tolerant attitude.

This study provides evidence of the difference in perceptions between men and women towards street harassment.

This study explores a relatively unexplored area, that is investigating street remarks in a non-Western context, where the demographic could have different perceptions towards street remarks.

This paper aims to determine the impact of perceived virtuality on team dynamics and outcomes by adopting the Input-Mediators-Outcome (IMO) framework. Further, it also investigates the mediating role of team processes and emergent states.

The authors collected survey data from 315 individuals working in virtual teams (VTs) in the information technology sector in India using both offline and online questionnaires. They performed the analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM).

The authors investigated two sets of hypotheses – both direct and indirect (or mediation interactions). Results show that psychological empowerment and conflict management are significant in managing VTs. Also, perceived virtuality impacts team outcomes, i.e. perceived team performance, team satisfaction and subjective well-being.

The interplay between the behavioural team process (conflict management) and the emergent state (psychological empowerment) was examined. The study also helps broaden our understanding of the various psychological variables associated with teamwork in the context of VTs.

Findings from this study will aid in assessing the consequences of virtual teamwork at both individual and organisational levels, such as guiding the design and sustainability of VT arrangements, achieving higher productivity in VTs, and designing effective and interactive solutions in the virtual space.

The study examined the interplay between behavioural team processes (such as conflict management) and emergent states (such as psychological empowerment). The study also theorises and empirically tests the relationships between perceived virtuality and team outcomes (i.e. both affective and effectiveness). It may serve as a guide to understanding team dynamics in VTs better.

This exploratory study attempts to enhance the current understanding of the research and practice of VTs within a developing economy.

This study investigates the optimum size for manufacturing firms and the impact of subcontracting on firms' likelihood of achieving their optimal scale in Vietnam.

Using data from the enterprise census in 2017 and 2021, the paper first estimates the production function to identify the optimum firm size for manufacturing firms and then, applies the logit model to investigate factors associated with the optimal firm size.

The study reveals that medium-sized firms exhibit the highest level of productivity. Nevertheless, a consistent trend emerges, indicating that nearly 90% of manufacturing firms in Vietnam operated below their optimal scale in both 2017 and 2021. An analysis of the impact of subcontracting on firms' likelihood to achieve their optimal scale emphasizes its crucial role, especially for foreign firms, exerting an influence nearly five times greater than that of the judiciary system.

The paper's findings offer crucial policy implications, suggesting that initiatives aimed at enhancing the overall productivity of the manufacturing sector should prioritise facilitating contract arrangements to encourage firms to reach their optimal size. These insights are also valuable for other countries with comparable firm size distributions.

This paper provides the first empirical evidence on the relationship between firm size and productivity as well as the role of subcontracting in firms' ability to reach their optimal scale in a country with a right-skewed distribution of firm sizes.

Background: Rising food bank usage in the UK suggests a growing prevalence of food insecurity. However, a formalised, representative measure of food insecurity was not collected in the UK until 2019, over a decade after the initial proliferation of food bank demand. In the absence of a direct measure of food insecurity, this article identifies and summarises longitudinal proxy indicators of UK food insecurity to gain insight into the growth of insecure access to food in the 21st century.

Methods: A rapid evidence synthesis of academic and grey literature (2005–present) identified candidate proxy longitudinal markers of food insecurity. These were assessed to gain insight into the prevalence of, or conditions associated with, food insecurity.

Results: Food bank data clearly demonstrates increased food insecurity. However, this data reflects an unrepresentative, fractional proportion of the food insecure population without accounting for mild/moderate insecurity, or those in need not accessing provision. Economic indicators demonstrate that a period of poor overall UK growth since 2005 has disproportionately impacted the poorest households, likely increasing vulnerability and incidence of food insecurity. This vulnerability has been exacerbated by welfare reform for some households. The COVID-19 pandemic has dramatically intensified vulnerabilities and food insecurity. Diet-related health outcomes suggest a reduction in diet quantity/quality. The causes of diet-related disease are complex and diverse; however, evidence of socio-economic inequalities in their incidence suggests poverty, and by extension, food insecurity, as key determinants.

Conclusion: Proxy measures of food insecurity suggest a significant increase since 2005, particularly for severe food insecurity. Proxy measures are inadequate to robustly assess the prevalence of food insecurity in the UK. Failure to collect standardised, representative data at the point at which food bank usage increased significantly impairs attempts to determine the full prevalence of food insecurity, understand the causes, and identify those most at risk.

The article aims at exploring the challenges and possibilities of cooperation of higher education in a Latin American social, political and cultural context that faces historical difficulties of integration, as well as the potential contribution of academic cooperation for global citizenship.

The paper presents a general overview of networks and international centers of academic cooperation of higher education in Latin America. The analysis comprises objectives, countries, stakeholders, activities, projects and scope. The study is based on literature on internationalization, regional integration and the development of higher education, as well as on empirical gathered with networks/centers and key actors in the field. This study was carried out as a mixed qualitative method design. Firstly, a systematic review of a literature corpus of studies produced by Latin-American scholars was performed. Semi-structured interviews were then carried out with a group of scholars who are members of networks.

The findings include a review of the role of higher education in a politically fragmented reality, a panorama of major networks and international centers of academic cooperation with emphasis on internationalization of higher education, as well as their connections. The are highlighted examples of successful initiatives of cooperation and, based on interviews, there is presented a preliminary view on cooperation and trust building from professionals in higher education in Latin America.

In the last decades, with the growing interest and need for internationalizing higher education, many universities have organized or joined networks and international centers. The article will contribute for mutual knowledge of these spaces, their shortcomings and potentials, thus creating conditions for dialogue among them, as well as with universities in other continents.

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 is one of the first studies to examine specificities, including limits of mindfulness at work in an African organisational context, whilst dealing with the ongoing COVID-19 pandemic. It specifically addresses the role of organisational and managerial support systems in restoring employee wellbeing, social connectedness and attachment to their organisations, in order to overcome the exclusion caused by the ongoing pandemic.

The study uses a qualitative research methodology that includes interviews as the main data source. The sample comprises of 20 entrepreneurs (organisational leaders) from Ghana and Nigeria.

The authors found that COVID-19-induced worries restricted the practice of mindfulness, and this was prevalent at the peak of the pandemic, particularly due to very tough economic conditions caused by reduction in salaries, and intensified by pre-existing general economic and social insecurities, and institutional voids in Africa. This aspect further resulted in lack of engagement and lack of commitment, which affected overall team performance and restricted employees’ mindfulness at work. Hence, quietness by employees even though can be linked to mindfulness was linked to larger psychological stress that they were facing. The authors also found leaders/manager’s emotional intelligence, social skills and organisational support systems to be helpful in such circumstances. However, their effectiveness varied among the cases.

This paper is one of the first studies to establish a link between the COVID-19 pandemic and mindfulness limitations. Moreover, it is a pioneering study specifically highlighting the damaging impact of COVID-19-induced concerns on leader–member exchange (LMX) and team–member exchange (TMX) relationships, particularly in the African context. It further brings in a unique discussion on the mitigating mechanisms of such COVID-19-induced concerns in organisations and highlights the roles of manager’s/leader’s emotional intelligence, social skills and supportive intervention patterns. Finally, the authors offer an in-depth assessment of the effectiveness of organisational interventions and supportive relational systems in restoring social connectedness following a social exclusion caused by COVID-19-induced worries.

Research excellency has long been the dominant paradigm in assessing academic quality and hence a prime determinant of academic careers. Lately, this approach to academic performance has come under higher scrutiny for its narrow focus on the individual, promoted an exclusive, performance-oriented talent management and inhibiting collaboration, transparency and societal involvement.

As a response to the limitations of the excellency policy, this chapter examines the emergence of open science as a transformative force in the academic world. Open science represents a paradigm shift, emphasizing the importance of transparency, and increased societal engagement in the academic process. It opens up the possibility to include the context dimension, multiple stakeholders and a more diverse set of development and performance indicators.

This chapter stresses the urgent need to realign our system of recognition and rewards with the premise of open science and with talent management. By highlighting the disconnect between current recognition mechanisms and the values of universities, this chapter emphasizes the necessity of transformative changes at institutional and systemic levels.

To provide concrete insights into the implementation of these changes, this chapter explores a case study of Utrecht University. This specific example showcases how strategic decisions at an institute level allow navigation of the complexities of recognizing and rewarding open science practices. The Utrecht University case study serves as an inspiration for other institutions seeking to embrace open science and adapt their policies and practices accordingly.

The study sought to examine the impacts of COVID-19 on tourism from the stakeholder perspective in the case of Famagusta town in Northern Cyprus.

Via a qualitative research approach, data are collected through face-to-face interviews from direct and indirect tourism stakeholders operating in Famagusta. A judgmental sampling strategy was employed to collect data from tourism stakeholders on the impacts of COVID-19 on tourism. Descriptive data analysis is engaged to report the results.

Results of the study showed that the novel coronavirus has hampered the tourism sector in Famagusta, Northern Cyprus. Indeed, as the globe suffered its effects in terms of economic gains, business and business closure. It has been the same with Famagusta, while a few private sectors positively gained (Internet and Technology), all other tourism-reliant sectors such as hotels, restaurants, travel agencies and the transport sector massively suffered as a result of the global lockdown due to COVID-19 pandemic.

Given the ever-changing state of knowledge and scarcity of literature, the current study seeks to summarize what has been learned from previous crises and back it up with qualitative research including senior industry stakeholders.