Contextual Knowledge Research Articles

Assessment of emotion knowledge in a bilingual context and its relationship with adaptive behavior in early childhood education

Multiple education organizations and experts, including the recent education law in Spain, emphasize the significance of nurturing emotional skills from childhood to prevent the development of emotional and behavioral disorders. One of these skills is emotion knowledge (EK), which is closely linked to personal and social adjustment from early years, and later psychosocial adaptation. The present study aims to provide an assessment tool for EK in the bilingual context of the Basque Country and to provide evidence about the relationship between EK and various behavioral outcomes in early childhood education. The research involved 455 children between 3 and 6 years of old. The Emotion Matching Task, which measures the EK, was adapted to the bilingual context of the Basque Country has shown good psychometric properties and appropriate factor fit. The study also revealed the factor invariance of the Spanish and Basque versions, as well as invariance by sex. The results demonstrated that girls have higher EK than boys. Additionally, EK was associated with fewer externalizing, internalizing, and attention problems, as well as with lower atypicality and withdrawal, while higher EK was linked to greater adaptive skills. These findings support the need for interventions focused on the development of emotional skills and the suitable tools to assess their effectiveness.

The Psychoanalytic Act in the Preschool Space, or How to Be Useful Instead of Right.

This contribution examines the position of expert knowledge in the institutional context of an American preschool and a consulting psychoanalyst's refusal to join in its unquestioned dynamic. It interrogates the shift, both theoretical and clinical, occurring if and when the authority of knowledge is supplanted by attention to transference. By arguing that a classroom is the space for the psychoanalytic act and by making a distinction between what it means to be useful rather than right, the author opens a perspective for psychoanalysis in extension that the cared-for children may welcome more than the caring adults.

Market orientation, integrated marketing communications, and small and medium-sized enterprises (SMEs) performance: A comparison between developed and developing economies

Drawing on dynamic capabilities theory, the study aims to contribute to the existing literature by investigating the mediating role of integrated marketing communication (IMC) in the relationship between market orientation (MO) and customer-related and market performance. Specifically, it focuses on understanding how IMC operates as a strategic tool for small and medium-sized enterprises (SMEs) in the international environment. The research uses data from 422 SMEs and large firms in both developed and developing economies. Hypothesis testing was conducted using structural equation modelling (SEM). The results show that IMC, as a dynamic capability, enhances the positive impact of MO on customer-related and market performance in both developed and developing economies. The firm's size moderates this mediation effect; however, the moderation varies depending on the economy type. In the developing economy, IMC mediation is stronger for SMEs than large firms, whereas in the developed economy company size doesn't impact IMC mediation. This research contributes to the marketing communications and business management theory, particularly by expanding the knowledge of the unique context of SMEs in developing economies. The practical implications of the study are particularly relevant for SMEs managers operating in diverse economic contexts, providing actionable insights that can inform decision-making and marketing strategies for SMEs.

Process deviations, early sensemaking, and enabling operators: Thinking beyond the traditional alarm-based practice to enhance industrial resilience

In the dynamic landscape of modern industrial systems, there is an emerging important need for new thinking and practice to facilitate risk-informed decision-making, especially in high-risk industrial sectors. Such novel solutions should enable operators to proactively counteract and prevent unwanted events and incidents. Efforts to harness and convert relevant data to meaningful information with unique meanings, in fact represents new opportunities to create relevant knowledge of the actual context. This paper examines this through early sensemaking of process deviations, facilitating a seamless integration of information towards context-sensitive decision-making processes. Two industrial approaches are discussed in the paper: a conventional alarm-based practice, as well as an enhanced approach. The research underscores the application of such techniques through two industrial cases, aiming at fostering enhanced resilience, reliability, and safety within high-risk industrial settings.

Insights of learning approach towards determination of potentially objectional communication in social networking

<p><span lang="EN-US">Over the last decade, sentiment analysis has evolved significantly towards extracting the contextual knowledge associated with the communication exchanged in social networks. Irrespective of various approaches to natural language processing and constantly evolving machine learning, sentiment analysis has inherent shortcomings, which further act as an obstacle to determining hateful and offensive speech exchanged in social networks. Therefore, this paper offers a compact yet granular insight into the effectiveness of existing sentiment analysis approaches used distinctly for determining hateful and offensive speech with particular emphasis on machine learning-based methodologies. The paper further contributes towards research trend analysis followed by distinct highlights of the research gap. The paper offers a learning outcome that significantly benefits future researchers investigating the same field.</span></p>

PROFESSIONAL INTELLECTUAL ABILITIES OF A FUTURE OFFICER: THEORETICAL TOOLS AND EMPIRICAL RESULTS

Relevance of the study. The development of professional intellectual abilities during training in higher education institutions is a relevant and modern scientific task. This is due to the fact that modern sectors of the economy require specialists with a high level of professional intelligence and systemic knowledge of their specialty. The technological and innovative nature of armed conflicts, military actions and special military operations require a future officer to have a highly developed intellect and the ability to effectively think in different conditions. The purpose of the study was a comprehensive study of the essence and structure of professional intellectual abilities of future officers of the National Guard troops in theoretical and practical aspects. Method and methodology. The declared comprehensive study of the essence and structure of professional intellectual abilities of future officers of the National Guard troops in theoretical and practical aspects was based on modern developments in the field of improving training in higher education institutions, the formation, education and development of personally significant and professional qualities of future officers, the objectification of the value-semantic sphere in the subject-social reality. The methods used in the work were general logical techniques and methods of scientific knowledge, as well as theoretical and empirical ways of obtaining scientific knowledge in a pedagogical context. Research results. the relevance of the problem was proven; the need to develop tools for developing professional intellectual abilities of future officers; provisions on the effectiveness of pedagogical conditions; the structure of professional intellectual abilities of a future officer and the essence of the pedagogical program for their development were revealed; scientific ideas about the modern educational environment as a factor in the development of professional intellectual abilities of future officers were expanded. Scope of application of the results. The scientific results of the work formed the basis for the development of the module «Development of professional intellectual abilities of an officer of the Russian Guard»; served as a scientific and methodological basis for clarifying the results obtained under the main educational program «Legal Support of National Security», «Translation and Translation Studies», «Military-Political Work».

The Effect of PDEODE and SWOM Strategies in Developing 10th Grade Literary Students’ Historical Thinking Skills

The current study which is an extract from a PhD dissertation adopted a mixed- method, experimental-design to find out the effect of PDEODE and SWOM strategies in developing tenth- grade literary students’ historical thinking skills. The sample of the study consists of sixty tenth grade students from the literary branch at Tafan Preparatory School for Girls. After equalizing them, they are divided in this way: Group A regarded as the first experimental group taught by PDEODE strategy, Group B as the control group taught conventionally and group C taught by SWOM strategy as the second experimental group. For each of these groups, a twenty- four daily teaching plan was used and a 30- item test was prepared by the researcher to know the effect of these strategies in developing historical thinking skills. After analyzing the data through the statistical method ONE- WAY ANNOVA, it was displayed that there are statistically significant differences among the groups in a way the first experimental group is better than the second experimental and control groups. It was also concluded that PDEODE and SWOM as two new teaching strategies can provide appropriate platform for improving teaching and learning processes as they focus on students’ intellectual, psychological and personal dimensions, making them the center of the process which enables them to apply their knowledge in different contexts.

The Role of Business Leaders in Promoting Sustainable Practices and the SDGS-Experience from India

Objective: The article aims to analyse the role of business leaders in India for promoting sustainability and SDGs as a management best practice, having due regard to the regulatory framework which subsist in India as on date. Method: The article is qualitative research, which examines how Indian corporates promote sustainability and SDG best practices which entails relevant contextual knowledge. Reliance has been placed on secondary data like relevant literature, article and case studies to co-relate with the research problem and resultant analysis. Result & Discussion: The research revealed the growing trend among Indian corporate leaders to recognise the importance of sustainability and SDGs as a part of corporate strategy. The enhancement in investments and advancement and advancements in renewable energy, sustainable supply chain management, waste management etc demonstrating this transition. It also notes the challenges from various stakeholders for such changes as a major challenge for the purpose. Originality/ Value: The study pioneeringly investigates the role of business leaders in promoting sustainability and SDGs as a business strategy in India.

Clinical use of autologous cell-based therapies in an evolving regulatory landscape: A survey of patient experiences and perceptions.

Background: Clinical treatments involving autologous cell-based therapies (ACBT) remain prevalent despite a lack of scientific backing and an evolving regulatory landscape aimed at assessing their safety and efficacy for clinical adoption. This study seeks to assess patients' experiences and perceptions of clinical treatments involving ACBT and their knowledge and views of the regulatory context and associated governance issues. Methods: An anonymous online survey of 181 participants who have been treated or are in the process of being treated with ACBT was conducted. Recruitment was via social media platforms. Data was collected through Qualtrics and analyzed using SPSS 29 for the quantitative responses and NVivo 1.7.1 for the qualitative responses. Results: Several themes emerged from the data, including the prominent role of healthcare providers throughout the patient journey, informational practices during the clinical encounter, the high prevalence of pay-for-participation trials, patients' gaps in regulatory knowledge, and patients' priorities regarding clinical trials and regulation of ACBT. Conclusions: The study makes a novel contribution to the literature by providing the first analysis of patients' experiences and perceptions of an emerging cell-based therapy within an evolving regulatory landscape. The findings serve as a valuable resource for developing policy, promoting scientific rigor, and ensuring ethical oversight of ACBT and other upcoming cell-based therapies.

Exploring users’ algorithmic knowledge and reflexivity in a music streaming context: A critical realist approach

Digital platforms such as Spotify have specific characteristics and properties that influence, to some extent, how the platform is used. However, users develop their own interpretations of these properties as well as unique ways to engage with the platform. This study applies a critical realist framework to explore how reflexivity modes are practiced in the context of Spotify as an example of algorithmic recommendation systems. From this perspective, reflexivity is a person’s capacity to reflect on their contexts, data, previous experiences, and knowledge, among other elements, before deciding how to act. Findings from interviews with Spotify users suggest that participants practice multiple reflexivity modes when interpreting Spotify’s recommendations and deciding what to listen to. These modes depend on each participant’s concerns and algorithmic knowledge.

Prediction-error signals in anterior cingulate cortex drive task-switching

Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states.

Multi-level feature interaction for open knowledge base canonicalization

Open Information Extraction (OpenIE) aims to construct expansive open knowledge bases (OKBs) by extracting triples (noun phrase, relation phrase, noun phrase) from unstructured text. One critical problem in OKBs is the lack of canonicalization for noun phrases and relation phrases, leading to the storage of redundant and ambiguous facts. Consequently, open knowledge base canonicalization, which clusters synonymous phrases into the same group, has emerged as an active research area. Existing approaches either leverage fact triples or source context in isolation, or at best, interact them at the clustering level. However, these approaches lack explicit interaction or only loosely couple the two types of knowledge, resulting in the potential loss of valuable intermediate information. In this paper, we propose MuFIC, a novel unsupervised framework that interacts the fact triples and source context at the feature level to address these limitations. In order to capture and integrate fine-grained fact and context knowledge, we design three levels of feature interaction: low-level context-guided feature interaction, mid-level fact-guided feature interaction, and high-level gated fusion feature interaction. Furthermore, we introduce an additional objective function via contrastive learning to improve the quality of extracted features and reduce knowledge-specific noise. Finally, we design a bidirectional feedback mechanism to better guide the learning process of joint features by harnessing side information prototype learning, and to dynamically optimize side information based on the clustering results formed by joint features. Extensive experiments on three public benchmark datasets demonstrate the superiority of our proposed framework.

A Complex Environmental Water-Level Detection Method Based on Improved YOLOv5m.

The existing methods for water-level recognition often suffer from inaccurate readings in complex environments, which limits their practicality and reliability. In this paper, we propose a novel approach that combines an improved version of the YOLOv5m model with contextual knowledge for water-level identification. We employ the adaptive threshold Canny operator and Hough transform for skew detection and correction of water-level images. The improved YOLOv5m model is employed to extract the water-level gauge from the input image, followed by refinement of the segmentation results using contextual priors. Additionally, we utilize a linear regression model to predict the water-level value based on the pixel height of the water-level gauge. Extensive experiments conducted in real-world environments encompassing daytime, nighttime, occlusion, and lighting variations demonstrate that our proposed method achieves an average error of less than 2 cm.

MCKP: Multi-aspect contextual knowledge-enhanced prompting for conversational recommender systems

Empowering conversational recommender systems (CRSs) with knowledge facilitates the generation of high-quality human-like recommendation proposals to users. Despite substantial endeavors in developing knowledge-based CRSs, they mainly focus on modeling entity knowledge by introducing external data, which cannot sufficiently contribute to understanding complex contextual semantics. Additionally, the source of knowledge relies on external data, ignoring the effective use of original training data, significantly increasing the cost of building efficient CRSs.To tackle the issues above, we explore how to leverage raw dialogue data for modeling and exploiting contextual knowledge to compensate for existing knowledge's shortcomings. Specifically, we first train knowledge generators by constructing multiple standard hybrid prompt templates and answer spaces, which is conducive to perceiving the reasons behind contextual connections from multiple aspects and storing corresponding contextual knowledge. Then, we design answer-guided prompt templates based on the answer space, closing the gap between inference and training of knowledge generators and facilitating the generation of aspect-specific contextual knowledge. Next, we extend our approach by harnessing external knowledge graphs to construct entity knowledge. We propose a local-to-global semantic fusion strategy to fuse knowledge representations from different sources better. Finally, based on the fused knowledge representations, we tailor contextual knowledge-enhanced prompt templates for the CRS to stimulate the reasoning ability of pre-trained language models on dialogue and recommendation tasks. We evaluate the performance of our method on separate evaluation datasets, including the ReDial and the INSPIRED datasets. Based on the Recall, MRR, NDCG, and Distinct evaluation indicators, we demonstrate the effectiveness of the proposed method in dialogue tasks and recommendation tasks. At the same time, we demonstrate that by constructing an interpretable multi-aspect hybrid prompt template, we can effectively mine contextual knowledge from multiple aspects of dialogue.

Locating TPACK XK between theory and practice: Reflective practice, applied ethics, and technoskeptical dispositions

The problem of context, contextualization, and XK has stalked TPACK since its inception. We suggest that efforts in education technology to deepen the theory and scholarship around this domain of knowing have run up against the field's tendency toward discrete interpretations of TPACK, its continued reluctance to explore socio-cultural influences on education and technology, and the inherent tensions between abstracted theoretical models and the realities of practice they represent. Additionally, context raises important ethical considerations that are deeply embedded in educational technology selection and use. In an attempt to reject the binary of theory and practice and also extend the relationships between contextual and ethical knowledge in TPACK, we propose ways the field can extend both research and practice related to XK through reflective practice, professional ethics, and technoskeptical dispositions. Using examples from classroom practices, we have developed reflective and critical questions to elicit connections between TPACK, XK, and ethics which include hyper-local and narrow contexts as well as broader socio-cultural contexts. Intersecting ethics with TPACK helps educators situate the profession of education within society; consider the broad social implications of technology, pedagogy, and content separately and as TPACK; and work toward commitments for learning and social justice through the actions of the good enough.

Uncovering the uncoverers: identity, performativity and representation in counter-disinformation discourse

ABSTRACT Treating counter-disinformation activities as a discursive practice producing knowledge in specific cultural and historical contexts, this article brings a vital cultural studies perspective to an incipient Critical Disinformation Studies agenda. At the same time, it cautions against inadvertently aligning that agenda with the ‘bad faith’, right-wing populist critique of counter-disinformation practices, acknowledging their genuine potential to enhance democratic culture. It proceeds by interrogating the communicative strategies informing the proliferating Western counter-disinformation initiatives which have mushroomed in response to what is widely perceived to be one of democracy’s most potent threats, and which border on a ‘moral panic’ serving to conceal key ideological tensions. Grounded in Critical Discourse Analysis methods, a close reading of mission statements and related materials from the websites of four leading counter-disinformation units demonstrates how these texts interweave three functions – identity construction, performativity, and representation – to generate the overarching ‘discursive formation’ or ‘articulation’ constituting counter-disinformation as modern liberal democracy’s core, but also exposing its fault lines. Specifically, it shows that a failure to reconcile competing accounts of truth (as scientific rationality and normative value) indexes deeper contradictions pitting the liberal emphasis on individual freedom and capitalist efficiency against the democratic prioritizing of popular power, dispassionate observation against civic participation, and the promotion of transparency against the creeping power of surveillance. It concludes that the absolutist epistemology – a vestige of disinformation’s Cold War conceptual origins – designed to cover over these paradoxes masks the complex roles played by truth and deception in politics, thereby hindering the emergence of alternative, oppositional forms of knowledge capable of embracing truth in its radical contingency and plurality and facilitating democratic renewal.

Leveraging local knowledge for landslide disaster risk reduction in an urban informal settlement in Manado, Indonesia

Disaster preparedness and response, including for landslides, increasingly involves local knowledge. Incorporating contextual, dynamic and experience-based local knowledge leads to greater awareness of the interconnectedness of geological, natural, and social processes. Still, technical literature on urban landslide risk is mainly based on geological and natural dynamics and, to a lesser extent, physical infrastructure. Moreover, although local knowledge is recognized as important in principle for all aspects of disaster risk management, it is not frequently taken as a starting point for identifying contextually specific landslide risk factors in practice. To gain insights into local knowledge on factors contributing to landslide risk, we conducted a qualitative case study within a landslide-prone informal settlement in Manado, Indonesia. The study comprised qualitative household interviews, transect walks, and ethnographic observation. Our findings indicate that anthropogenic factors specific to informal settlements, particularly inadequate solid waste management, may serve as an overlooked risk factor for urban landslides. This is especially problematic in low-income or informal neighborhoods, where detrimental effects of poor solid waste management frequently intersect with heightened vulnerability to various hazards. We therefore advocate systematically incorporating anthropogenic factors into studies of contributing factors to urban landslides. Additionally, we encourage further research into the interactions between inadequate solid waste management and landslide risk. Finally, we call for leveraging local knowledge to enhance policy, planning, and research efforts, with the ultimate goal of fostering safer urban environments for all.

Bridge the gap between simulated and real-world data in optical fiber mode decomposition for accuracy improvement: A deep learning-based co-learning framework with visual similarity-based matching

In the area of optical fiber mode decomposition (MD), data-driven deep-learning (DL) algorithms have achieved remarkable strides with unlimited theoretically synthetic data, but the differences between theoretical and real-world data in the DL context are rarely investigated. The two differences, namely the information bottleneck and the domain discrepancy, between the simulation and realistic domain remain significant obstacles that limit the practical deployment of DL-based MD approaches in real-world optical systems. The paper aims to mitigate their influence on the accuracy limitation and the accuracy degradation of DL-based MD. First, we present a DL-based co-learning framework for MD for the first time. With the co-learning process, the knowledge of the teacher model learned from rich input information sources (near-field, far-field, and phase-distribution optical images) is transferred to the student model with the only near-field input source, which breaks the information bottleneck by allowing the student model to learn more knowledge in a simulation context. Furthermore, to settle the domain discrepancy between ideal data and practical observation, we propose a visual similarity-based matching to assign real values of modal coefficients to the practical optical images whose ground truth values cannot be easily acquired, which enables the transfer learning from the synthetic data to the real-world data for accuracy enhancement. Simulated and experimental results show that the proposed co-learning framework with visual similarity-based matching has realized high precision and robustness in MD simulated and practical tasks. By bridging the gap between theoretical and practical data, our study offers novel perspectives on DL-based MD practice.

DESIGNING VISUAL SYMBOLS IN DIGITAL CULTURE

Designing visual symbols is crucial in the ever-changing world of digital culture for communication, identity building, and cultural expression. With an eye toward the aesthetic, functional, and semiotic aspects, this article delves into the methods and principles of making effective visual symbols in digital settings. The study delves into a wide array of digital symbols, including emojis, logos, icons for user interfaces, and digital art, to uncover how these visual components communicate meaning, impact user behavior, and aid in forming online identities and communities. Recognizing digital spaces' multicultural and global character, the research highlights the significance of cultural sensitivity and contextual knowledge throughout the design phase. Additionally, it delves into how technological innovations, including augmented reality and vector graphics, have influenced the development of visual symbols. The adaptability of symbols in response to user interactions and cultural trends may be shown in case studies from various digital platforms and social media networks. The results stress the importance of designers balancing originality and clarity in creating visually appealing and universally understandable symbols. At the end of the paper, we suggest ways to make symbols that may change with digital culture and appeal to different audiences by using iterative design processes and user feedback.

The Influence of Mother Tongue Thinking on Second Language Writing

In the process of foreign language understanding, learning and using, thinking in the mother tongue will inevitably lead to the intervention of strong mother tongue contextual knowledge in the brain, and then activate the mother tongue expressions that match the mother tongue contextual knowledge. The object of second language acquisition is influenced by age, the current study set out to examine to what degree of acquisition (AOA), defined as a learner's first intensive exposure to a second language (L2) environment, mediates the final state of poster, spoken vocal accommodation. The more the students use mother tongue thinking in English writing, the shorter the writing text. This negative correlation is most prominent in the text-generation activities of writing. In fact, similar experimental findings were found in early studies. For intermediate learners, the longer the code-switching takes place in second-language writing, the shorter the text will be. Therefore, the research topic of this paper is the influence of mother tongue thinking transfer on second-language writing.