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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.

The purpose of this study is to examine the quality of information in articles in the online encyclopedia, Wikipedia, as perceived by readers and content contributors. This study explored several dimensions and characteristics of information presented in Wikipedia by identifying new emerging dimensions in terms of readers’ perceptions of the quality of online information.

Two rounds of online surveys were conducted using a mixed-method approach. In the first survey, the authors conducted content analysis on 197 participants, and in the second survey, the authors conducted factor analysis on 107 study participants. The authors used Qualtrics Panel Services to recruit individuals who read and/or edited the English version of Wikipedia articles and resided in the United States.

The mixed-method approach employed in this study to explore the quality of online information had three core components: users’ perceptions of information quality, content analysis, and exploratory factor analysis of the perceived information quality structure. The study found a new information quality category, social aspect quality. Dimensions include fun, goodness, empowering and user generated.

The results demonstrate the emergence of novel quality attributes for information quality presented online, particularly in social media. Moreover, this study is one of the rare studies to employ a mixed-method approach that offers diverse but reliable perspectives on information quality as perceived by everyday users of Wikipedia.

Wikipedia's inclusive editorial policy permits unrestricted participation, enabling individuals to contribute and disseminate their expertise while drawing upon a multitude of external sources. News media outlets constitute nearly one-third of all citations within Wikipedia. However, embracing such a radically open approach also poses the challenge of the potential introduction of biased content or viewpoints into Wikipedia. The authors conduct an investigation into the integrity of knowledge within Wikipedia, focusing on the dimensions of source political polarization and trustworthiness. Specifically, the authors delve into the conceivable presence of political polarization within the news media citations on Wikipedia, identify the factors that may influence such polarization within the Wikipedia ecosystem and scrutinize the correlation between political polarization in news media sources and the factual reliability of Wikipedia's content.

The authors conduct a descriptive and regression analysis, relying on Wikipedia Citations, a large-scale open dataset of nearly 30 million citations from English Wikipedia. Additionally, this dataset has been augmented with information obtained from the Media Bias Monitor (MBM) and the Media Bias Fact Check (MBFC).

The authors find a moderate yet significant liberal bias in the choice of news media sources across Wikipedia. Furthermore, the authors show that this effect persists when accounting for the factual reliability of the news media.

The results contribute to Wikipedia’s knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

Using a discourse analytic approach, the purpose of this paper is to examine how genre impacts white readers when reading about historic acts of racial violence. Specifically, this study explores one white high school student’s stance-taking as she read an informational text and an eyewitness narrative about the Tulsa Race Massacre.

This study used discourse analysis (Gee, 1999) and the think-aloud method (Pressley and Afflerbach, 1996) to explore the white student’s interactions with genres of historical texts. The authors coupled iterative coding and memoing with discourse analysis to analyze the stances she adopted while reading.

The findings illustrate that the informational text allowed for a distancing from the racialized violence in the text, whereas the narrative created an opportunity for more connection to those who experienced the violence.

While genre and reader response has long been explored in English Education research, little research has examined the impact of genre on reading historical texts. This study demonstrates the influence that genre may have on white readers’ emotional responses and stance-taking practices when reading about historic acts of racial violence.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

This study aims to explore the strategies corporations use in engaging stakeholders to sustain healthy corporate partnerships and create value for the corporate entity and the society in which they operate and their influence on the corporate manager’s cognitive abilities and decision-making.

The authors used an interpretive research approach leveraging the strengths of qualitative method of content analysis and comparative and critical analyses to report the results. Interpretive methods incorporate social theories and standpoints that view reality as the social construction of understandable events in the context of organizational communication.

The findings of this study suggest that corporations are assumed to follow and execute the principles of engaging stakeholders to achieve corporate social responsibility (CSR) claiming to manage a sustainable and responsible business practices that recognize local cultures, human rights and protect the environment. However, little attention has been paid to the cognitive reasoning of the individuals responsible for CSR and corporate sustainability (CS) as opposed to the growing concerns about strategies corporations use in engaging stakeholders to sustain healthy corporate partnerships and create value – especially the processes that take place during engagement and decision-making including cognitive offloading.

Stakeholder engagement requires practical approaches that enable corporations and individuals charged with decision-making responsibilities to understand, respond and fulfill their CSRs. To achieve CSRs, corporations and managers responsible for relevant decision-making would need to involve stakeholders in social performance planning, as social reporting/auditing has long been advocating for preventing managerial biasness, groupthink and increased information dissemination via detailed reporting practices toward more collaborative stakeholder relationships. Thus, it is crucial for corporations to implement enhanced stakeholder and managerial decision-making strategies such as integrative approaches to achieve balance in the trio elements of sustainability as well as the growing use of paradox perspective to understand the nature of the tensions being sought to balance and, in the process, provide opportunity for a better evaluation of complex sustainability issues for innovative approach to resolving them. While cognitive decision-making is at play, in practice, managers tasked with making decisions must ensure the most effective stakeholder engagement strategies that are transparent and inclusive are used.

The main contribution of this study is its argument regarding the tools corporations use in engaging key stakeholders and the cognitive reasoning of the individuals responsible for CSR and CS. The study further contributes to interpreting the integrative approach to achieving balance in the trio elements of sustainability as well as the growing use of paradox perspective to understand the nature of the tensions being sought to balance and, in the process, provide an opportunity for a better evaluation of complex sustainability issues for an innovative approach to resolving them.

This study aims to discover how domain familiarity and language affect the cognitive load and the strategies applied for the evaluation of search engine results pages (SERP).

This study used an experimental research design. The pattern of the experiment was based upon repeated measures design. Each student was given four SERPs varying in two dimensions: language and content. The criteria of students to decide on the three best links within the SERP, the reasoning behind their selection, and their perceived cognitive load of the given task were the repeated measures collected from each participant.

The evaluation criteria changed according to the language and task type. The cognitive load was reported higher when the content was presented in English or when the content was academic. Regarding the search strategies, a majority of students trusted familiar sources or relied on keywords they found in the short description of the links. A qualitative analysis showed that students can be grouped into different types according to the reasons they stated for their choices. Source seeker, keyword seeker and specific information seeker were the most common types observed.

This study has an international scope with regard to data collection. Moreover, the tasks and findings contribute to the literature on information literacy.

This paper aims to present recently published resources on library instruction and information literacy. It provides an introductory overview and a selected annotated bibliography of publications organized thematically and detailing, study populations, results and research contexts. The selected bibliography is useful to efficiently keep up with trends in library instruction for academic library practitioners, library science students and those wishing to learn about information literacy in other contexts.

This article annotates 340 English-language periodical articles, dissertations, theses and reports on library instruction and information literacy published in 2022. The sources were selected from the EBSCO platform for Library, Information Science and Technology Abstracts (LISTA), Education Resources Information Center (ERIC), Elsevier SCOPUS and ProQuest Dissertations and Theses. Sources selected were published in 2022 and included the terms “information literacy,” “library instruction,” or “information fluency” in the title, subject terms, or author supplied keywords. The sources were organized in Zotero. Annotations were made summarizing the source, focusing on the findings or implications. Each source was then thematically categorized and organized for academic librarians to be able to skim and use the annotated bibliography efficiently.

The paper provides a brief description of 340 sources from 144 unique publications, and highlights publications that contain unique or significant scholarly contributions. Further analysis of the sources and authorship are provided.

The information is primarily of use to academic librarians, researchers, and anyone interested as a quick and comprehensive reference to literature on library instruction and information literacy published within 2022.

This paper refines the Digital Media–Arena (DMA) framework to address the diversity of stakeholders contributing to the production, (re)appropriation and (re)distribution of organisational messages in digital environments. It also presents a case analysis for the purpose of demonstrating the applicability of the revised conceptual framework to a critical situation.

Grounded in key public relations, corporate communication and strategic communication research, this study first extends the DMA framework by introducing six new forms of media-arenas. Next, the study takes a public sector perspective to analyse the revised framework against a critical situation involving the Finnish prime minister in summer 2022.

The application of the revised DMA framework to analyse the critical situation shows the importance of mapping and understanding diverse discourses across multi-arenas and their communication role in a rapidly unfolding scandal surrounding the prime minister of Finland. Findings also reveal the diversity of stakeholder voices forming their own versions of organisational messages and sometimes converging organisational messages within and across DMAs.

The DMA framework can offer practical suggestions to guide communicators to make strategic choices in what, where, how and with whom they can communicate.

The revised DMA framework contributes expanding the field's knowledge of the strategic communicative use of the digital environment in typically highly volatile and multi-vocal situations by offering instrumental understanding of the conflicting challenge between subjugating and liberating organisational messages across the digital spectrum.

In this chapter, the authors envision a new framework for technology-enabled local engagement. This framework would exploit web-based collaboration technology in order to create local engagement panels that represent a cross section of ordinary residents. For concreteness, the authors illustrate the framework in a proposed study called “Clearing the Air” would enable residents in the areas of Southern California that are most impacted by the logistics industry to have substantive and constructive opportunities to engage local officials on the complex economic and environmental matters related to logistics and emissions in the Southern California region. The authors propose methods to evaluate best practices in the use of collaboration technology, in particular, to learn whether the technology enables and empowers the engagement panels to participate in air quality governance at a high level and helps local officials better understand the considered opinions of residents on these important matters. If successful, this framework would integrate the policy views of individual residents alongside those of organized stakeholders, experts, and agency officials in the policy process.