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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 study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

This paper investigates the challenges and opportunities for the deployment of whistleblowing as an accountability mechanism to curb corruption and fraud in a developing country. Nigeria is the institutional setting for the study.

Adopting an institutional theory perspective and a survey protocol of urban residents in the country, the study presents evidence on the whistleblowing program introduced in 2016. Nigeria’s whistleblowing initiative targets all types of corruption, including corporate fraud.

This study finds that, even in the context of a developing country, whistleblowing is supported as an accountability mechanism, but the intervention lacks awareness, presents a high risk to whistleblowers and regulators, including the risk of physical elimination, and is fraught with institutional and operational challenges. In effect, awareness of whistleblowing laws, operational challenges and an institutional environment conducive to venality undermine the efficacy of whistleblowing in Nigeria.

The study presents a model of challenges and opportunities for whistleblowing in a developing democracy. The authors argue that the existence of a weak and complex institutional environment and the failure of program institutionalization explain those challenges and opportunities. The authors also argue that a culturally anchored and institutionalized whistleblowing program encourages positive civic behavior by incentivizing citizens to act as custodians of their resources, and it gives voice to the voiceless who have endured decades of severe hardship and loss of dignity due to corruption.

This paper aims to synchronize the various constitutional regulations that regulate the natural disaster management in Indonesia, especially those which apply in the Yogyakarta Special Territory after disaster through a legal interpretation and construction method to find a community empowerment-based disaster management model, which suits the Indonesian ideals of law.

This research is carried out in the Yogyakarta Special Territory province; this research uses the juridical normative method or the method with the doctrinal or the juridical normative approach. The approaches used in this research are the conceptual approach, statute approach and the sociological approach.

The numerous constitutional regulations that are formed and implemented to regulate the disaster management in Yogyakarta Special Territory cannot yet run its function as an integrating mechanism efficiently. This is mainly because the handling of disasters is usually responsive, without clear planning.

In numerous constitutional regulations, there is a synchronization between the regulations on the society’s rights and responsibilities in disaster management. The point of these regulations is that they state that every citizen has the right to obtain social protection and a sense of safety. They have the right to obtain education, trainings and skills in the establishment of disaster management. Also, they have the right to participate in policies, in accessing information on disaster prevention policies.

Efforts of response toward a disaster should be neither exclusive nor partial. A condition of disaster is a complex condition, which usually asks for a holistic response from various perspectives and experiences. It needs effective teamwork between various institutional groups. Basically, it will not be effective if it is run by a single agency exclusively. Indonesia needs a clear disaster management and needs to synchronize the law for disaster mitigation for minimize the natural disaster impact.

Various constitutional regulations made and applied to regulate disaster management in the Yogyakarta cannot yet run its function as an efficient integrating mechanism, as the law cannot yet undergo the rearrangement of the productive process in the society optimally. The goals determined in the execution of the disaster management are often not legitimized by the society, and they do not yet give a full sense of justice to them. Recovery after Yogyakarta earthquake is a slow process.

This is a relatively new research, as other researches focused on the disastrous impacts of the Yogyakarta earthquake. The disaster management system must consider and must be responsive toward diversity, differences and competition, which may arise due to social, economic, political, community and even religious factors. These differences often create a dynamic and complex relation. A wrong manner in handling this may cause horizontal conflicts.

This study attempts to figure out the factors that contributed to deposing certain elected presidents before the end of their constitutional terms, alongside tracing the new political context that prevailed in Latin America since 1978 and its impact on direct political participation and military behavior during presidential crises.

The paper uses the comparative method to investigate the causes of presidential instability in three case studies.

The likelihood of presidential instability increases when a president enacts austerity economic policies that marginalize large sectors of the citizenry, becomes implicated in acts of corruption and develops a hostile relationship with members of the ruling coalition.

This study integrates the social movement theory with analytical perspectives from parliamentary behavior to explain presidential instability. It attempts to investigate the dynamics of interaction between the acts of furious citizens and disloyal legislators through the in-depth analysis of three case studies.

This paper aims to present a brief discussion on the geopolitical aspects of diplomatic divergences between India and Bangladesh regarding the Rohingya issue. Presently, more than a million people are living in 30 refugee camps in Bangladesh. In August 2017, the plight of Rohingya refugees broke all the previous record and had largely affected the Cox's Bazar region of Bangladesh when Bangladesh decided to provide shelter to the Rohingyas, identifying them as “Forcibly Displaced Myanmar Nationals (FDMN). Due to geographical closeness and historical linkage with both Myanmar and Bangladesh, India, despite taking strict measures to avoid any cross-border opportunities for Rohingyas, could not escape the consequences. Myanmar, due to its strategic position and natural resources, has always been the epicentre of attention and investment of foreign powers. The crisis has all the elements in it to create political turbulence in South Asia and South East Asia. A peaceful environment based on mutual trust and cooperation is required for the continuing economic growth of the region. Considering the importance of in-depth research in this arena, the study pursued the qualitative method.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.