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Tourism in Coimbra today is influenced by the fact that the Univer(s)city was distinguished as a World Heritage Site in 2013. The number of visits has grown very significantly in recent years, but the diversification of the tourist offer is still weak and unable to take advantage of existing resources. This paper aims to present genealogy tourism as an alternative urban cultural tourism in Coimbra.

Methodology involved mapping the Jewish culture elements in the city of Coimbra, and a route was outlined and proposed.

Genealogy tourism resources are identified in the historic centre of the city. These alternative spaces need urban rehabilitation and (re)functionalisation, which allowed the authors to rethink tourism in Coimbra. They are the motivation to visit for all urban cultural tourists, especially Israelis/Jews, and provide contact with places where the experiences of ancestors combine with the history and memory of places, with recent discoveries and the elements of Jewish culture in the city.

It is concluded that the quantity, diversity, authenticity and singularity of the heritage resources that bear witness to the Jewish presence in Coimbra are sufficient assets to create a route, to enrich the tourist experience in the city and to include the destination in the Sephardic routes.

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.

The paper aims to examine agrochemical traders’ tax morale in three Ghanaian regions.

Primary data were collected from 92 respondents using structured questionnaires. A multistage sampling technique was employed and used in selecting respondents.. Descriptive statistics, factor analysis and quantile regression analysis were used to analyse data obtained via the questionnaires.

The study found tax reporting knowledge, tax calculating knowledge and tax payment knowledge to be the keen factors influencing agrochemical traders’ tax knowledge. It was also revealed that age, religion and marriage positively influence the tax morale of traders. Inversely, gender, high level of education and monthly sales were found to affect tax morale negatively. Moreover, trust (respect, trustworthiness and expertise knowledge) negatively influenced tax morale. Authorities’ tax knowledge and power (sanction and lockdown) were revealed to impact tax morale positively. However, tax morale decreases amongst agrochemical traders with higher tax morale when sanction increases.

Unlike previous studies which focussed on tax morale amongst individuals and firms outside the agribusiness sector, this study examined the tax morale within the informal agrochemical trading sector, which has recently attracted colossal patronage due to the high usage of agrochemicals amongst farmers in Africa and Ghana. This study also assumed tax morale to be at different levels; hence the factors that affect the morale at different levels differ. Therefore, the study examined the factors influencing tax morale amongst agrochemical traders by segregating tax morale into quartiles. Relating to theory, the economic deterrence theory was used to ground the study, which is not usually used in most tax morale studies.

The aim of this paper is to evaluate contrasting ways of tackling self-employment in the informal sector. Conventionally, the participation of the self-employed in the informal sector has been viewed as a rational economic decision taken when the expected benefits outweigh the costs, and thus enforcement authorities have sought to change the benefit-to-cost ratio by increasing the punishments and chances of being caught. Recently, however, neo-institutional theory has viewed such endeavor as a product of a lack of vertical trust (in government) and horizontal trust (in others) and pursued trust-building strategies to nurture voluntary compliance.

To evaluate these contrasting policy approaches, data are reported from special Eurobarometer survey 92.1 conducted in 2019 across 28 European countries (the 27 member states of the European Union and the United Kingdom) involving over 27,565 interviews.

Using probit regression analysis, the finding is that the likelihood of participation in informal self-employment is not associated with the level of expected punishments and chances of being caught, but is significantly associated with the level of vertical and horizontal trust, with a greater likelihood of participation in informal self-employment when there is lower vertical and horizontal trust.

The outcome is a call for state authorities to shift away from the use of repressive policy measures that increase the penalties and chances of being caught and toward trust-building strategies to nurture voluntary compliance. How this can be achieved is explored.

Evidence is provided to justify a shift toward seeking trust-building strategies by state authorities to engender voluntary compliance among the self-employed operating in the informal sector in Europe.