Search results

The purpose of this study is to investigate the needs of potential end-users of a database dedicated to Swedish lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI) literature (e.g. prose, poetry, drama, graphic novels/comics, and illustrated books), in order to inform the development of a database, search interface functionalities, and an LGBTQI thesaurus for fiction.

A web questionnaire was distributed in autumn 2021 to potential end-users. The questions covered people's reasons for reading LGBTQI fiction, ways of finding LGBTQI fiction, experience of searching for LGBTQI fiction, usual search elements applied, latest search for LGBTQI fiction, desired subjects to search for, and ideal search functionalities.

The 101 completed questionnaires showed that most respondents found relevant literature through social media or friends and that most obtained copies of literature from a library. Regarding desirable search functionalities, most respondents would like to see suggestions for related terms to support broader search results (i.e. higher recall). Many also wanted search support that would enable retrieving more specific results based on narrower terms when too many results are retrieved (i.e. higher precision). Over half would also appreciate the option to browse by hierarchically arranged subjects.

This study is the first to show how readers of LGBTQI fiction in Sweden search for and obtain relevant literature. The authors have identified end-user needs that can inform the development of a new database and a thesaurus dedicated to LGBTQI fiction.

The purpose of this paper is to review and discuss Luciano Floridi’s 2019 book The Logic of Information: A Theory of Philosophy as Conceptual Design, the latest instalment in his philosophy of information (PI) tetralogy, particularly with respect to its implications for library and information studies (LIS).

Nine scholars with research interests in philosophy and LIS read and responded to the book, raising critical and heuristic questions in the spirit of scholarly dialogue. Floridi responded to these questions.

Floridi’s PI, including this latest publication, is of interest to LIS scholars, and much insight can be gained by exploring this connection. It seems also that LIS has the potential to contribute to PI’s further development in some respects.

Floridi’s PI work is technical philosophy for which many LIS scholars do not have the training or patience to engage with, yet doing so is rewarding. This suggests a role for translational work between philosophy and LIS.

The book symposium format, not yet seen in LIS, provides forum for sustained, multifaceted and generative dialogue around ideas.

The purpose of this paper is to formulate an analytical framework for the information concept based on the semiotic theory.

The paper is motivated by the apparent controversy that still surrounds the information concept. Information, being a key concept within LIS, suffers from being anchored in various incompatible theories. The paper suggests that information is signs, and it demonstrates how the concept of information can be understood within C.S. Peirce’s phenomenologically rooted semiotic. Hence, from there, certain ontological conditions as well epistemological consequences of the information concept can be deduced.

The paper argues that an understanding of information, as either objective or subjective/discursive, leads to either objective reductionism and signal processing, that fails to explain how information becomes meaningful at all, or conversely, information is understood only relative to subjective/discursive intentions, agendas, etc. To overcome the limitations of defining information as either objective or subjective/discursive, a semiotic analysis shows that information understood as signs is consistently sensitive to both objective and subjective/discursive features of information. It is consequently argued that information as concept should be defined in relation to ontological conditions having certain epistemological consequences.

The paper presents an analytical framework, derived from semiotics, that adds to the developments of the philosophical dimensions of information within LIS.

The study consists of a literature study and a case study. The need for a method via which to handle instruction complexity was identified in both studies. The proposed method was developed based on methods from the literature and experience from the case company.

The purpose of the study presented in this paper is to investigate how linking different maintenance domains in a modular maintenance instruction architecture can help reduce the complexity of maintenance instructions.

The proposed method combines knowledge from the operational and physical domains to reduce the number of instruction task variants. In a case study, the number of instruction task modules was reduced from 224 to 20, covering 83% of the maintenance performed on emergency shutdown valves.

The study showed that the other methods proposed within the body of maintenance literature mainly focus on the development of modular instructions, without the reduction of complexity and non-value-adding variation observed in the product architecture literature.

This paper adds to the literature on societal grand challenges by shifting the focus away from business firms and other formal organizations as key actors in addressing such challenges toward the inherent organizing capacity that lies in the use of language itself. More specifically, we focus on the organizing capacities of metaphor-based communication, seeking to ascertain which qualities of metaphors enable them to co-orient collective action toward tackling grand challenges. In addressing this question, we develop an analytical framework based on two qualities of metaphorical communication that can provide such co-orientation: a metaphor’s (a) vividness and (b) responsible actionability. We illustrate the usefulness of this framework by assessing selected metaphors used in the public discourse to make sense of and organize collective responses to the Covid-19 pandemic, including the flu metaphor/analogy, the war metaphor, and the combined metaphor of “the hammer and the dance.” Our paper contributes to extant research by providing a means to assess the co-orienting potential of metaphors in bridging varied interpretations. In so doing, our framework can pave the way toward more responsible use of metaphorical communication in tackling society’s grand challenges.

The purpose of this paper is to make a critical analysis of the views put forward by Claudio Gnoli (2018) in this paper concerning philosophical problems in library and information science (LIS).

The paper presents the basic ideas in Gnoli (2018) and discusses the set of basic assumptions, concepts and conclusions put forward.

It is argued that the idea of the theory of levels is basically sound, but we do not need to consider the material world, the mental world (minds) and the world of mentefacts as three different worlds. They represent different levels with different kinds of emergent properties in the world. Further, although the concepts of artifacts and mentefacts are useful, there are other terms within LIS, such as document, work and object that have been influential and should be discussed in this context. It is also argued that subjective vs objective knowledge is often confused with private vs public knowledge, which is problematic. Finally, it is claimed that the cognitive view and the “sociological view” are not about two different levels of reality but are competing views about the same reality.

The paper clarifies some aspects of the analytical framework of domain analysis and adds to the developments of the philosophical dimensions of information within LIS.

The purpose of this paper is to advance the understanding of sound branding by developing a new conceptual framework and providing an overview of the research literature on non-musical sound.

Using four mutually exclusive and collectively exhaustive types of non-musical sound, the paper assesses and synthesizes 99 significant studies across various scholarly fields.

The overview reveals two areas in which more research may be warranted, that is, non-musical atmospherics and non-musical sonic logos. Moreover, future sound-branding research should examine in further detail the potentials of developed versus annexed object sounds, and mediated versus unmediated brand sounds.

The paper provides important insights into critical issues that suggest directions for further research on non-musical sound branding.

The paper identifies an unexploited terrain of possibilities for the use of sound in marketing and branding.

The paper identifies a subfield within sound-branding research that has received little attention despite its inevitability and potential significance.

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.