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Ethical principles and practices frequently support the position that people with disability are vulnerable. Vulnerability in research traditionally infers a need for protection from harm and raises questions over the person’s capacity to consent and engage. In addition, vulnerability in ethics infers a state of permanency and one that is all-encompassing for everyone within the vulnerable groups. This construction of vulnerability in effect legitimises the exclusion of people with disability from research or monitors and restricts how people with disability can engage in research. This results in an implicitly ableist environment for research. In this chapter, which has been led by researchers with disability, we argue that there is a critical need to move beyond a popularised social construction of vulnerability which serves to perpetuate barriers to including people with disability in research. Like all terms, the traditional and popular construction of vulnerability is open to reclaiming and reframing. Under this reconstruction, what is traditionally viewed as a limiting vulnerability can be owned, openly disclosed and accommodated. Following a pandemic-inspired ‘new normal’ that supports flexible workplace practices, and in accordance with UNCRPD goals of inclusive employment and reducing disability inequity, we argue that the pathway for people with disability as career researchers needs an ethical review and overhaul. We provide readers with a practical roadmap to advance a more inclusive academy for researchers with disability.

Environmental disasters are preventable, but this remains a complicated and elusive prospect. This article discusses factors that combine to limit and undermine environmental disaster prevention efforts and explores directions for improved theory and practice.

The challenge of integrating root cause analysis of environmental disasters with interventions and preventive measures at later stages of disaster incubation is outlined. The prospect of learning and transferring lessons from past environmental disasters is discussed. Eighteen environmental disaster cases are summarized and analyzed.

A range of factors, including complexity, lack of lesson transfer, perceived lack of incentives and inaction, limits advances in environmental disaster prevention. Theoretical challenges involve better bridging of root cause and incubation analyses, enhanced understanding of the nature and discipline of foresight and greater documentation of alternative approaches to prevention, including post–normal techniques. Although a transformative breakthrough in environmental disaster prevention is unlikely, substantial progress could be made through better lesson transfer and application of alternative approaches.

This article draws attention to problems and opportunities surrounding the challenge of environmental disaster prevention and proposes directions for improved theory and practice.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

This study has two main objectives. First, to examine the indirect effects of digital platform capability and digital resilience on digital transformation (DT) outcomes for small- and medium-sized enterprises (SMEs), and second, to investigate how digital business model maturity influences these indirect effects.

The study adopts a quantitative design and collects data through a self-reporting survey from individuals in the technological industries. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) and PLS multi-group analysis examine the measurement and structural models and the significance of differences in indirect paths based on the digital business model maturity level, serving as a moderator.

The findings of this study provide valuable insights into the internationalisation of digital SMEs. They indicate that digital platform capability and resilience fully mediate, connecting digital resources to SME growth. The study also confirms the digital business model maturity’s positive and significant moderating effect on these indirect relationships.

This research contributes to the existing literature by focusing on the international outcomes of platform ecosystems in developing markets. It explores how digital platform capability and resilience support the digital transformation of SMEs, considering their vulnerability due to their small size. The study also fills a research gap by investigating the relationship between big data, digital leadership and the international growth of digital platforms. Lastly, it explores the role of digital maturity in the relationships between antecedents, determinants and outcomes of digitalisation.

The purpose of this paper is to present the views of people living with dementia in extra care housing (ECH). This is a model of housing with care and support aiming to support older people, including those with dementia, to live independently. Previous research identifies benefits but is predominantly derived from third-party accounts, with the voices of those living with dementia in ECH significantly absent.

This study adopted a qualitative approach conducting 100 interviews across 8 ECH schemes in England. Over half of the interviews were conducted with people living with dementia and their families with the remainder involving staff and commissioners.

Findings suggest there are a range of benefits including owning your own home, having a safe, age friendly location with flexible support, social interaction and continuing to live as a couple. Challenges included availability of staff, flexible resourcing, loneliness and the advancing symptoms of dementia.

Despite efforts to create an inclusive, diverse sample, the participants were all White British. Participants involved were identified by gatekeepers, which may present some bias in the selection.

Whilst ECH offers benefits to people living with dementia, addressing the challenges is essential for effective dementia care. Improving staff training, promoting person-centred care and fostering an inclusive community are critical for enhancing residents’ well-being and quality of life.

This paper explored the lived experiences of residents and family members, providing new insight into the advantages and disadvantages of ECH for people living with dementia.

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.