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This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Although there is a growing body of work on immigrants' information behavior, little is known about the pre-arrival information experiences of immigrants who consult formal information sources such as immigration agents. Drawn from a larger study on the information behavior of immigrants, this paper mainly reports the semi-structured interview findings on the pre-arrival information experiences of Bangladeshi immigrants who used formal information sources with discussion on how that affected their post-arrival settlement into Canada.

The study used a mixed method approach with semi-structured interviews (n = 60) and surveys (n = 205) with participants who arrived in Canada between the years of 1971 and 2017. Data were collected from May 2017 to February 2018.

Although the overall scope of the original study is much larger, this paper features findings on the pre-arrival information experiences derived mainly from an analysis of interview data. This study provides insights into the pre-arrival information experiences of Bangladeshi immigrants consulting formal information sources such as immigration firms, individual immigration consultants and more formal government agencies. The author introduces a new concept of “information crafting” by exploring the negative consequences of selective information sharing by immigration consultants/agents in newcomers' settlements in Canada, primarily positive information about life in Canada, sometimes with exaggeration and falsification. The interview participants shared story after the story of the settlement challenges they faced after arriving in Canada and how the expectations they built through the information received from immigration consultants and government agencies did not match after arrival. This study emphasizes the importance of providing comprehensive information about life in Canada to potential newcomers so that they can make informed decisions even before they apply.

The findings of this study have theoretical and practical implications for policy and research. This study provides insights into the complicated culturally situated pre-arrival information experiences of Bangladeshi immigrants. Moreover, the study findings encourage researchers in various disciplines, including psychology, migration studies and geography, to delve more deeply into newcomers' information experiences using an informational lens to examine the information newcomers receive from diverse sources and their effects on their post-arrival settlement in a new country. The study challenges the general assumptions that formal information sources are always reputable, useful, and comprehensive, and it provides some future directions for research that seeks to understand the culturally situated information behavior of diverse immigrant groups.

This study addresses how algorithmic recommendations and their affordances shape everyday information practices among young people.

Thematic interviews were conducted with 20 Finnish young people aged 15–16 years. The material was analysed using qualitative content analysis, with a focus on everyday information practices involving online platforms.

The key finding of the study is that the current affordances of algorithmic recommendations enable users to engage in more passive practices instead of active search and evaluation practices. Two major themes emerged from the analysis: enabling not searching, inviting high trust, which highlights the how the affordances of algorithmic recommendations enable the delegation of search to a recommender system and, at the same time, invite trust in the system, and constraining finding, discouraging diversity, which focuses on the constraining degree of affordances and breakdowns associated with algorithmic recommendations.

This study contributes new knowledge regarding the ways in which algorithmic recommendations shape the information practices in young people's everyday lives specifically addressing the constraining nature of affordances.

This paper aims to present an interactive design and simulation tool for permanent magnet synchronous machines based on the finite-element-method. The tool is intended for education and research on electrical machines.

A coupling between the software MATLAB and finite element method magnetics is used. Several functionalities are included as modular scripts and represented in the form of a graphical user interface. Included are fully parametrized motor models, automatic winding generations and the evaluation of torque waveforms, core losses and speed-torque-diagrams. A survey was conducted to determine how the motivation of students concerning the covered topics is influenced by using the tool.

Due to its simplicity and the intuitive visualization of the results, the tool provides direct access to the topic of electrical machines without having to deal with separate scripts. The modular structure of the software allows simple extensions with new functions. Because students can directly contribute to the tool with their own work, their motivation for using and extending it increases.

The presented tool offers more functionalities compared to similar free software packages, e.g. the calculation of core losses and speed-torque diagrams. Also, it is designed in such a way that it can be easily understood and extended by students.

This research paper aims to delve into the perceptions of health susceptibility among Iranian, Afghan and Tajik individuals hailing from asylum-seeking or refused asylum-seeking backgrounds currently residing in Finland, Norway and Sweden.

Semi-structured interviews were conducted between May and October 2022 involving a sample size of 27 participants. An adapted framework based on the health belief model along with previous studies served as a guide for formulating interview questions.

Notably influenced by cultural background, religious beliefs, psychological states and past traumatic experiences during migration journeys – before arrival in these countries till settling down – subjects’ perception of health concerns emerged significantly shaped. Additionally impacting perspectives were social standing, occupational status, personal/family medical history, lifestyle choices and dietary preferences nurtured over time, leading to varying degrees of influence upon individuals’ interpretation about their own wellness or illness.

Insights garnered throughout the authors’ analysis hold paramount significance when it comes to developing targeted strategies catering culturally sensitive health-care provisions, alongside framing policies better aligned with primary care services tailored explicitly around singular demands posed by these specific communities dwelling within respective territories.

This investigation represents one among few pioneering initiatives assessing perceptions regarding both physical and mental well-being within minority groups under examination across Nordic nations, unveiling complexities arising through intersecting factors like individual attributes mingling intricately with socio-cultural environments, thereby forming unique viewpoints towards health-care belief systems prevalent among such population segments.

This study was conducted to address the methodical shortcomings and high associated cost of understanding the use of new, poorly understood architectural spaces, such as makerspaces. The proposed quantified method of enhancing current post-occupancy evaluation (POE) practices aims to provide architects, engineers and building professionals with accessible and intuitive data that can be used to conduct comparative studies of spatial changes, understand changes over time (such as those resulting from COVID-19) and verify design intentions after construction through a quantified post-occupancy evaluation.

In this study, we demonstrate the use of ultra-wideband (UWB) technology to gather, analyze and visualize quantified data showing interactions between people, spaces and objects. The experiment was conducted in a makerspace over a four-day hackathon event with a team of four actively tracked participants.

The study shows that by moving beyond simply counting people in a space, a more nuanced pattern of interactions can be discovered, documented and analyzed. The ability to automatically visualize findings intuitively in 3D aids architects and visual thinkers to easily grasp the essence of interactions with minimal effort.

By providing a method for better understanding the spatial and temporal interactions between people, objects and spaces, our approach provides valuable feedback in POE. Specifically, our approach aids practitioners in comparing spaces, verifying design intent and speeding up knowledge building when developing new architectural spaces, such as makerspaces.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.