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Peer-to-Peer Accommodation Service (P2PAS) has emerged as a novel paradigm that enables consumers to book temporary accommodation through P2PAS platforms (online transaction), and then reside in hosts' rooms (offline consumption). Due to potential variance in performance and conflict of interest between hosts and platforms, consumers may differ in their trust perceptions of the two parties, which in turn affects consumers' continuous usage of P2PAS. To this end, the authors endeavor to unravel the effect of consumers' trust incongruence on continuance intention, and to further elucidate the moderating influence of transaction and consumption risks on this relationship. This paper aims to discuss the aforementioned objectives.

This study collected data through an online survey of 408 P2PAS consumers. Polynomial modeling and response surface analysis were conducted to validate the hypothesized relationships.

Response surface analysis reveals that trust incongruence did not significantly affect consumers' continuance intention. However, continuance intention would be greater when TP was higher than TH compared with when TH was higher than TP. Furthermore, the analytical results suggest that trust incongruence exerts greater negative effect on continuance intention when transaction and consumption risks were high.

First, the study marks a paradigm shift in conceptualizing the incongruence between TP and TH as a determinant of consumers' continuance intention toward P2PAS. Second, the authors derive a typology of risks that is contextualized to P2PAS. Finally, the authors establish transaction and consumption risks as boundary conditions influencing the effects of trust incongruence on consumers' continuance intention toward P2PAS.

Identity, positioning and possibilities intersect differently for South Asian women in white academia. Within a broader migrant community that defines Australian life, these identities and positioning imply great possibility, but pursuing such pathways within academia is a walk on the last strand of resilience. This paper explores this tension of possibilities and constraints, using hope theory to highlight the cognitive resistance evident in the narratives of three South Asian women in Australian academia.

The authors use collaborative autoethnography to share their narratives of working in Australian universities at three different stages of careers, utilising Snyder's model of hope theory to interrogate their own goal-setting behaviours, pathways and agentic thinking.

The authors propose that hope as a cognitive state informs resistance and enables aspirations to contribute within academia in meaningful ways whilst navigating the terrain of inequitable structures.

The authors' use of hope theory as a lens on the intersectional experiences of career making, building and progression is a new contribution to scholarship on marginalised women in white academe and the ways in which the pathways of resistance are identified.

This chapter traces the origin of racism and reviews the historical and contemporary debates around race and racialisation in western thought. There are persistent disagreements surrounding the origin and nature of racism. Because of the evolution of racist ideas, behaviours and institutional practices and policies, there are various views about the meaning and analytical application of racism. This chapter explores how ideas of race – understood as innate and immutable human differences that can be classified and ranked hierarchically based on race – has emerged in western history and evolved over time. It examines how this has influenced social and political practices and associated policies across the evolution of modernity. The chapter specifically discusses the Atlantic slave trade and how it shaped the historical development of race and racism within the context of colonialism. It concludes with a discussion and critical review of some of the racist systems and policies which have been enforced across different multiracial countries.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing.

The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term.

Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.

Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services.

This concluding chapter summarises the main themes and topics discussed in this book, synthesising the key issues facing contemporary anti-racism efforts. It reflects on a possible anti-racist future(s) in a context of greater sociocultural affiliations and more interconnected local and global environments. Ideas about race and ethnicity have adapted, and racial hierarchies, structures and processes continuously shape the way social groups engage, interact and live with difference. This raises questions regarding the enduring influence of race and racism. What will the state of multiracial societies be in the evolving digital economy that has transformed the structural and institutional environment affecting everyday life? What kind of an anti-racist future can be imagined that will contribute to ensuring greater social equity? This chapter ponders on a range of possibilities to chart directions towards an anti-racist future that fosters increased intercultural understanding for relational engagements across difference. It draws conclusions and lessons for an anti-racist future and lays out some directions for future research.

This chapter examines the concepts of race and racism, critically reviewing their historical and contemporary applications in everyday life as well as in academic and policy debates. Racism has been extensively researched, with various theories and conceptualisations developed across social science. However, there is a great deal of disagreement regarding its nature, contemporary significance and empirical validation. This chapter examines these and attempts to synthesise some of the common definitions of racism provided in the literature. It explores related concepts and underlying themes pertaining to expressions of race and racism. Furthermore, it unpacks current knowledge about racial issues and discusses recent advances in the conceptual understanding of various forms of racism. It also elucidates the social, political and analytical applications of racism as a concept and the significance of racism in contemporary societies. The chapter concludes by highlighting how racism is a dynamic phenomenon, continuously evolving with the social, political and technological transformations in contemporary societies.

While technical vocational education training (TVET) has been studied in-depth, the evolution and performance patterns of the subject remain unknown and limited. A bibliometric analysis was performed to examine the global scientific literature to assess the state of the art in TVET research over the past 23 years.

The Web of Science (WoS) database was searched to explore TVET-related research from 1999 to 2021, resulting in the identification of 7,512 articles. The VOSviewer software was used to investigate the network of collaboration between authors, institutions, countries and author keywords.

The results reveal that the subject categories of “education” and “educational research” are the most prolific contributors to TVET-related research, with 3,314 articles. Most of the previous studies in Phase I (1999–2006) focussed on human capital resources development in the TVET sector. Phase II (2007–2014) follows with the centralisation of TVET, focussing on technology transition in education. However, in Phase III (2015–2021), researchers appear to focus on vocational studies in higher education towards increasing the productivity of human resources via the implementation of technology transition.

The valuable findings of this study can facilitate better understanding among scholars on the trends of TVET research developments and on the direction of future research.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

The purpose of this paper is to provide a transcript of a dialogue among literacy educators and researchers on the impact of generative aritficial intelligence (AI) in the field. In the spring of 2023, a lively conversation emerged on the National Council of Research on Language and Literacy (NCRLL)’s listserv. Stephanie initiated the conversation by sharing an op-ed she wrote for Atlanta Journal-Constitution about the rise of ChatGPT and similar generative AI platforms, moving beyond the general public’s concerns about student cheating and robot takeovers. NCRLL then convened a webinar of eight leading scholars in writing and literacies development, inspired by that listerv conversation and an organizational interest in promoting intergenerational collaboration among literacy scholars.

As former doctoral students of two of the panel participants, webinar facilitators Grace and Victoria positioned themselves primarily as learners about this topic and gathered questions from colleagues, P-16 practitioners and those outside the field of education to assess the concerns and wonderings that ChatGPT and generative AI have raised. The following webinar conversation was recorded on two different days due to scheduling conflicts. It has been merged and edited into one dialogue for coherence and convergence.

Panel participants raise a host of questions and issues that go beyond topics of ethics, morality and basic writing instruction. Furthermore, in dialogue with one another, they describe possibilities for meaningful pedagogy and critical literacy to ensure that generative AI is used for a socially just future for students. While the discussion addressed matters of pedagogy, definitions of literacy and the purpose of (literacy) education, other themes included a critique of capitalism; an interrogation of the systems of power and oppression involved in using generative AI; and the philosophical, ontological, ethical and practical life questions about being human.

This paper provides a glimpse into one of the first panel conversations about ChatGPT and generative AI in the field of literacy. Not only are the panel members respected scholars in the field, they are also former doctoral students and advisors of one another, thus positioning all involved as both learners and teachers of this new technology.