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This paper aims to focus on academic library managers’ learning practices in the context of cooperative work supported by computational artefacts. Academic library managers’ everyday work is mainly cooperative. Their cooperation is supported predominantly by computational artefacts. Learning how to use the computational artefacts efficiently and effectively involves understanding the changes in everyday work that affect managers and, therefore, it requires deep understanding of their cooperative work practices.

Focused ethnography was conducted through participant observations, interviews and document analysis. Ten managers from a university library in Sweden participated in the research. A thematic method was used to analyse the empirical material. Computer supported cooperative work (CSCW) and work-integrated learning was used as the conceptual lens.

Five learning practices were identified: collaboration, communication, coordination, decision-making processes and computational artefacts’ use. The findings show that learning is embedded in managers’ cooperative work practices, which do not necessarily include sufficient training time. Furthermore, learning was intertwined with cooperating and was situational. Managers learned by reflecting together on their own experiences and through joint cooperation and information sharing while using the computational artefacts.

The main contribution lies in providing insights into how academic library managers learn and cooperate in their everyday work, emphasizing the role of computational artefacts, the importance of the work context and the collective nature of learning. It also highlights the need for continual workplace learning in contemporary knowledge work environments. Thus, the research generates contributions to the informatics field by extending the understanding of managers’ work-integrated learning in their everyday cooperative work practices supported by computational artefacts’ use. It also contributes to the intersection of CSCW and work-integrated learning.

This article examines curatorial practices, both traditional and digital, in the Guizhou Provincial Museum’s ethnic exhibition to assess their effectiveness in representing ethnic minority cultures, fostering learning and inspiring curiosity about ethnic textiles and costumes and associated cultures. It also explores audience expectations concerning digital technology use in future exhibitions.

A mixed-methods approach was employed, where visitor data were collected through questionnaires, together with interviews with expert, museum professionals and ethnic minority textile practitioners. Their expertise proved instrumental in shaping the design of the study and enhancing the overall visitor experience, and thus fostering a deeper appreciation and understanding of ethnic minority cultures.

Visitors were generally satisfied with the exhibition, valuing their educational experience on ethnic textiles and cultures. There is a notable demand for more immersive digital technologies in museum exhibitions. The study underscores the importance of participatory design with stakeholders, especially ethnic minority groups, for genuine and compelling cultural representation.

This study delves into the potentials of digital technologies in the curation of ethnic minority textiles, particularly for enhancing education and cultural communication. Ethnic textiles and costumes provide rich sensory experience, and they carry deep cultural significance, especially during festive occasions. Our findings bridge this gap; they offer insights for museums aiming to deepen the visitor experiences and understanding of ethnic cultures through the use of digital technologies.

This viewpoint paper aims to explore the past, present and future of travel visas granting permission to travel. Visa restrictions are used by governments as an efficient method of restricting access in advance of travel. This paper explores how this may change in the future resulting in a shift of power from tourist to destination.

The Futures Triangle method was used to create a scenario incorporating the three dimensions of the triangle, i.e. the pulls of the future, the pushes of the present and the weights of the past. An artefact of the future was created to help visualise this future.

This analysis suggests that the role of visas may change in the future such that visa regimes may become part of a destination strategy. A future scenario is postulated in which destinations demand proof of fit with the destination strategy before granting a visa.

This viewpoint paper develops an artefact of the future based on the changing role of travel visas. It suggests that tourists might need to market themselves to the destination, proving that they are a good destination fit, before they are granted a visa to travel.

Effective use of data across public health organisations (PHOs) is essential for the provision of health services. While health technology and data use in clinical practice have been investigated, interactions with data in non-clinical practice have been largely neglected. The purpose of this paper is to consider what constitutes data, and how people in non-clinical roles in a PHO interact with data in their practice.

This mixed methods study involved a qualitative exploration of how employees of a large PHO interact with data in their non-clinical work roles. A quantitative survey was administered to complement insights gained through qualitative investigation.

Organisational boundaries emerged as a defining issue in interactions with data. The results explain how data work happens through observing, spanning and shifting of boundaries. The paper identifies five key issues that shape data work in relation to boundaries. Boundary objects and processes are considered, as well as the roles of boundary spanners and shifters.

The study was conducted in a large Australian PHO, which is not completely representative of the unique contexts of similar organisations. The study has implications for research in information and organisational studies, opening fields of inquiry for further investigation.

Effective systems-wide data use can improve health service efficiencies and outcomes. There are also implications for the provision of services by other health and public sectors.

The study contributes to closing a significant research gap in understanding interactions with data in the workplace, particularly in non-clinical roles in health. Research analysis connects concepts of knowledge boundaries, boundary spanning and boundary objects with insights into information behaviours in the health workplace. Boundary processes emerge as an important concept to understand interactions with data. The result is a novel typology of interactions with data in relation to organisational boundaries.

The aim of the article is to develop a framework for assessment for contemporary commercial music (CCM) in a music department that is still organised in a traditional way. Assessment of CCM in tertiary institutions and external graded music exams were described and analysed, followed by the presentation of an assessment framework.

This research design is a content analysis. Data was collected through purposive sampling of primary sources of CCM syllabi. These syllabi are used descriptively and analytically for comparison.

The major findings from the content analysis are presented as an assessment framework for CCM. The assessment framework has implications for teaching and learning vocal CCM at both undergraduate and postgraduate in higher education.

The study focused on the development of assessment criteria for CCM, in a music department where no degree specialisation for CCM exists, and CCM is manifested in the principal instrument only. The framework for assessment may be useful to practitioners and academics who are attempting to introduce CCM in classically oriented music departments and where external constraints prohibit the introduction of a degree specialising in CCM.

This article presents an authentic assessment framework for CCM, that considers its style and performance practices.

The purpose of this article is twofold. First, this study characterises the current state of the bio-packaging market's development. Second, it identifies key factors influencing and possible scenarios of the bio-packaging market transition to increase the market share of compostable packaging.

The results of 29 in-depth interviews (IDIs) with representatives of the key groups of bio-packaging supply chains' (SCs') stakeholders were the input for the consideration of the research problem.

The main economic, legal, social and technological enablers and barriers to the bio-packaging regime transition are recognised, and their impact at the market level is explained. The authors recognised the hybrid transition scenario towards an increase in the market share of compostable packaging related to the three traditional pathways of transformation, reconfiguration and technological substitution.

This study contributes to a better understanding of the socio-technical system theory by examining interdependencies between landscape (external environment), market regime (bio-packaging market) and niche innovations (compostable packaging) as well as system transition pathways. The findings and conclusions on bio-packaging market developments can be important lessons learnt to be applied in different countries due to the same current development stage of the compostable packaging lifecycle worldwide.

Our study aims to focus on the application of knowledge mapping to provide pedagogically-structured learners' competences.

We conducted an experiment examined the associations between the pedagogical quality of students’ pedagogically-informed knowledge (PIK) maps, class assignment scores and perceptions of PIK mapping’s uses.

The results showed that higher assignment scores were significantly predicted by higher quality PIK maps, ratings for PIK mapping were significantly higher than other mappings, and the learners’ experience of PIK mapping led to a significant change of attitude towards mapping as a learning activity and to a positive opinion of the value of PIK mapping in particular. Interestingly, there was no significant relation between learners’ opinion ratings of the uses of PIK mapping in learning and their assignment scores.

Questions remain on the generalizability of the findings, and on the features of a PIK map which are particularly useful to a learner. This study investigated the value of PIK mapping in the context of a practical class on the building of simple DIY (do-it-yourself) holographic projectors; it may be thought that the applied nature of the topic was more suited to the PIK mapping of learner competences and intended learning outcomes than a more theoretic classroom topic on holography. A future study is planned to address this issue.

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 understand how practitioners use their insights in software development models to share experiences within and between organizations.

This is a qualitative study of practitioners in software development projects, in large-, medium- or small-size businesses. It analyzes interview material in three-step iterations to understand reflexive practice when using software development models.

The study shows how work processes are based on team members’ experiences and common views. This study highlights the challenges of organizational learning in system development projects. Current practice is unreflective, habitual and lacks systematic ways to address recurring problems and share information within and between organizations. Learning is episodic and sporadic. Knowledge from previous experience is individual not organizational.

Software development teams and organizations tend to learn about, and adopt, software development models episodically. This research expands understanding of how organizational learning takes place within and between organizations with practitioners who participate in teams. Learnings show the potential for further research to determine how new curriculums might be formed for teaching software development model improvements.

This paper explores flexible working practices (FWPs) and takes a critical view that argues a need to consider not only access to digital technological resources but also the vast array of factors that constrain one’s ability to use technology for its intended benefits, as constituting the digital divide post-COVID-19 lockdown.

Using a critical evaluation of the extant literature, we engage in a conceptual undertaking to develop theoretical propositions that form the basis for future empirical undertakings. To theoretically ground the arguments raised, we deploy the ontological lens of actor-network theory to illuminate the socio-technical dimensions of the digital divide in light of FWPs.

Since the COVID-19 pandemic, the need to adopt socially distanced work practices has become a reality for many organisations. We find that the adoption of FWPs, enabled by digital technologies, simultaneously signals hidden inequalities. We also develop a conceptual framework which depicts user responses in different technology environments that can either be limiting or enabling for individuals’ work productivity.

With regards to the digital divide, attention has often focused on access to digital technologies, as the term “digital divide” portrays. The implication is that the array of factors and resources that individuals are heterogeneously networked to, which also constitute the digital divide, is often taken for granted. We take a different ontological view that brings to the fore other factors at play within an individual’s network of relations.

本文擬探索彈性工作安排,並以批判性的觀點、去論證當我們探討2019冠狀病毒病封鎖解除後的數字鴻溝究竟是由什麼組成時,我們必須考慮數字技術資源的使用權限,以及關注各個有關的因素,因這些因素限制了我們從應用技術獲得預期效益的能力。

我們對現存文獻作批判性的評價,藉此參與一項概念性的工作,以便能建立可以成為將來實證性工作基礎的理論命題;為使提出的論點能得到理論上的依據,我們使用行動者網絡理論的本體論鏡頭,去闡明鑒於彈性工作安排的數字鴻溝的各個社會技術維度。

由於2019冠狀病毒病大爆發的關係,許多機構和組織都因實際情況而需採用保持社交距離的工作安排。我們發現、當我們採用有賴數字技術的發展而成行的彈性工作安排時,我們同時也會使不為人知的不平等狀況浮現出來;我們也注意到、不同的技術環境會限制個人的工作生產力,又或相反地助其建立工作生產力;就此,我們建立了一個可以描繪就這些不同的技術環境、用戶會如何回應的概念框架。

就數字鴻溝而言,人們的關注總放在數字技術使用權限的差異上,就如數字鴻溝這個術語所塑造的形象一樣。這種關注可能帶來的影響是即使有不同的因素和資源、即使個人會以各種形式與不同的因素和資源聯繫起來,而這些因素和資源構成了數字鴻溝,人們卻把這些因素和資源視為理所當然。我們採用不同的本體論觀點,希望藉此使個人關係網絡內起著作用的其它因素能突顯出來、免被忽視。