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Considers the introduction of OCLC and other technology in public libraries as a response to reduced budgets and an increased demand for information. Discusses the history of cataloguing, the development of new cataloguing systems, bibliographic record editing, and methods of processing materials. Surmises that OCLC has been a sound investment in forging a new link between acquisitions and cataloguing, as well as reducing cost per cataloguing unit.

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.

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

This article investigates Departmental representations of allies and enemies, especially in the Pacific Ocean, during the Great War. The first section provides an overview of the Department’s principal instruments “the School Paper and Education Gazette” in communicating representations as well as expected views and behaviours with regard to Empire, allies and enemies. The second section explores the Department’s positioning of Germany in the Pacific Ocean and in relation to Australia; the third looks at France; and both focus on children’s responses to the reporting. The final section investigates representations of New Zealand including those within the Australian and New Zealand Army Corps legend that the Department chose to acknowledge.

Purpose: To investigate best practices pertaining to tourism resilience and develop a tourism resilience framework for post-COVID-19 Bangladesh from the tourism destination management perspective.

Methodology: A qualitative research approach involving an integrative literature review was used in this research. Thirty-two scholarly works were considered in a thematic data analysis conducted to inform the development of a framework for post-COVID-19 Bangladesh.

Findings: Tourism resilience will be imperative, particularly in the areas of ecology, society, economy, and engineering, on institutional and individual levels. The proposed framework includes government, organization, social, and individual level resilience as the specific measures for Bangladesh. The particular nature of COVID-19 cases in Bangladesh, resulting in a relatively low death toll amidst a high-density population, works as a supportive ground in favor of resilience in the post-COVID-19 reality.

Practical Implications: The integrated framework is a theoretical contribution to post-COVID-19 tourism resilience literature on Bangladesh. It will operate as a policy guideline for key stakeholders when implementing initiatives to revive the tourism sector in the changed reality of Bangladesh after the pandemic.

Originality: The framework presented in this paper is the first attempt of its kind and can facilitate effective economic rebuilding in the changed postpandemic reality. The framework can be generalized to apply to other developing countries as well.

Paper Type: Theoretical framework development.

On every side voices are raised against the growing use of chemical additives in food; the possible hazards to health; the inadequacy of present methods of control and of stemming the rate at which the practice is growing. Not unexpectedly at the season of annual conferences, with its crop of wildish statements and scare headlines, attention appears to be focussed upon the problem as if it were something new. These platform heroics not‐withstanding, it is indeed a difficult and growing problem. Not by any means a new one, however, for additives have been used in food preparation for many years, but before the first War they were mainly natural products; large‐scale food processing had yet to come. Now synthetic products have replaced the natural and possible ill‐effects are engaging world interest.

The war between formidable rivals for the use of the word “Champagne” continues. It began in 1958 at the Old Bailey with a prosecution brought under the Merchandise Marks Act alleging the application of a false description, viz., “Spanish Champagne” to goods and a second charge of applying the false description “champagne.” For the prosecution it was stated that “champagne” could only come from the Champagne district of France, which in 1921 the French Government had officially established and limited as the sole area for the production of champagne. Such a description applied to a Spanish wine, therefore, was false and misleading. The prosecution failed. The judge had stated there was overwhelming evidence of wines having lost the territorial origin of their names.

Service industries hold an increasingly dynamic and pivotal role in today’s knowledge‐based economies. The logistics industry is a classic example of the birth and development of a vital new service‐based industry, transformed from the business concept of transportation to that of serving the entire logistical needs of customers. Quantum advances in science, technology, and communication in the new millennium have compelled firms to consider the potential of the so‐called new “resources” (technology, knowledge and relationship networks) that are essential if firms are to operate effectively within the emerging business model, and to utilise the opportunities to innovate and gain market leadership. Through an extensive literature review, this paper examines the factors that nurture innovation in logistics services, identifies the contributions of the new “resources” and, using industry examples, examines the application of these resources to logistics firms as they assume an extended role within the new business model.

The Fourth Industrial Revolution has escalated innovation to new heights unseen, creating an evolution of innovation and corporate social responsibility (CSR), and as a result, a more Innovative CSR. With this evolution comes also the evolution of the ‘Preneur’ from social entrepreneur to corporate social entrepreneur and corporate social intrapreneur. It is therefore important to acknowledge that social entrepreneurship is not just for the social sector, or start-up entrepreneur – corporations can also be social entrepreneurs. This chapter establishes an understanding of this possibility alongside solving wicked problems and challenges, and how to provide collaborative networks and co-creation experiences to assist others on this journey. More importantly, the chapter discusses how corporates can assist millennials (and Generation Z) by funding and incubating their innovative or social enterprise idea under the umbrella of CSR strategy, until it is ready to be released to the world. The chapter is supported by academic literature and business publications with suggestions for future research opportunities.