Hasil untuk "Discourse analysis"

Altin Sihombing

The doctrine of salvation (soteria) has historically stood at the center of Christian theology, shaping not only doctrinal identity but also ethical and communal life. Yet in postcolonial and plural contexts, classical Western formulations require contextual rearticulation through dialogue with indigenous wisdom. This study examines Poda Na Lima, the Batak Toba moral-ethical system, as a theological resource for reinterpreting Christian soteriology in North Sumatra, Indonesia. Poda Na Lima - literally “five instructions” - articulates principles of harmony, wisdom, discipline, respect, and knowledge that have guided Batak communal life across generations. Drawing on a qualitative theological-ethnographic methodology, the research integrates two complementary sources: textual analysis of Batak oral traditions and cultural codifications, and fieldwork among Huria Kristen Batak Protestan congregations, including participant observation and conversations with pastors and elders. Data were analyzed through three stages: hermeneutical reading of Poda Na Lima in relation to biblical themes of repentance, reconciliation, and transformation; doctrinal correlation with Christological and soteriological affirmations; and contextual theological synthesis that integrates Batak ethical wisdom with Christian doctrine. Findings demonstrate that Batak Christians reinterpret Poda Na Lima not merely as cultural heritage but as a living moral compass aligned with Christological virtues of humility, service, and obedience. When placed in dialogue with soteriology, Poda Na Lima enriches the understanding of salvation as both divine redemption and ethical-communal transformation. The study concludes that indigenous wisdom, far from being peripheral, can function as a theological interlocutor, offering a contextual soteriology that affirms the integrity of Batak culture while contributing to global theological discourse on salvation and human flourishing.

Seyyed Mohammad mahdi Mousavimehr

In recent years, the Arbaeen ritual of Husseini has become a phenomenon beyond religious and Shiite rituals and has spread widely at the regional and global levels. This ceremony, as a social, political, cultural and media mega-event, has been the subject of attention and analysis by various news organizations. It is important to examine and understand the way this event is represented in various media. This research was conducted using the critical discourse analysis method of Fairclough and Van Dyke. Its theoretical foundations are based on the examination of linguistic structures, discursive strategies and ideologies governing the media's news and analytical texts regarding Arbaeen and are based on three levels of description, interpretation and explanation in discourse analysis and the use of the theories of Hall and Gramsci. The findings indicate that the news coverage of Arbaeen in the media, rather than being a close reflection of reality, is an active discursive representation that seeks to influence public opinion and shape the audience's perception. Finally, the four discourses of Persian-speaking media abroad with a negative and controversial approach, mainstream media with a moderate approach and relative neutrality, Arab media with an independent approach and sometimes in the form of regional competition, and aligned media with a spiritual, epic, and unifying approach have been identified, and an attempt has been made to explore and criticize the hidden layers of these discourses through textual and discursive analysis.

Caroline Reinhammar

This article examines the discourse surrounding wind-power establishment in Sweden, analyzing how common sense is mobilized in opposition to planned projects. The analysis draws on media coverage of opinion pieces. Identifying three prominent forms of articulation, the study highlights how these narratives frame wind turbines as symbols of conflict – between local environmental concerns, regional identity and democratic influence, and financial stability. Drawing on a critical folkloristic perspective, the article explores the complex interaction between local sentiments and broader political discourses, illustrating how local transition conflicts function as arenas for ideological struggle and the articulation of competing values. Criticism of market-driven mechanisms may enhance civic involvement; however, there is a parallel risk that such mobilization will be subsumed into larger political discourses designed to obstruct climate transformation. Under these conditions, the climate crisis and democratic accountability may increasingly be recast as elements of an expanding “culture war” narrative.

Yaniv Yacoby

We present a new undergraduate ML course at our institution, a small liberal arts college serving students minoritized in STEM, designed to empower students to critically connect the mathematical foundations of ML with its sociotechnical implications. We propose a "framework-focused" approach, teaching students the language and formalism of probabilistic modeling while leveraging probabilistic programming to lower mathematical barriers. We introduce methodological concepts through a whimsical, yet realistic theme, the "Intergalactic Hypothetical Hospital," to make the content both relevant and accessible. Finally, we pair each technical innovation with counter-narratives that challenge its value using real, open-ended case-studies to cultivate dialectical thinking. By encouraging creativity in modeling and highlighting unresolved ethical challenges, we help students recognize the value and need of their unique perspectives, empowering them to participate confidently in AI discourse as technologists and critical citizens.

Yulia Kuznetsova

These are edited notes of my mini-course given at the Analysis and PDE center of the University of Ghent, Belgium, in November 2024.

Viet Hoan Ngo

This article applies the postcolonial criticism frameworks of Edward Said and Gayatri Spivak to examine five representative novels by Mo Yan: The Herbivorous Family, Red Sorghum Clan, Sandalwood Death, Big Breasts and Wide Hips, and Life and Death Are Wearing Me Out. The analysis focuses on how Mo Yan’s narratives construct Eastern images, grotesque aesthetics, and female images under the influence of postcolonial discourse. The findings reveal that his depictions of violence, the human body, and patriarchal oppression simultaneously reflect a complex negotiation with Western cultural hegemony and an inheritance of traditional Chinese narrative paradigms. By situating Mo Yan’s literary strategies within broader postcolonial debates, this study demonstrates the multidimensional nature of postcolonial discourse in contemporary Chinese literature. Moreover, it highlights how these works may inform comparative approaches to Vietnamese literature particularly regarding issues of national identity, gender, and the reception of global literary discourses.

Aviva Ruch, Joel Francis, Ann Z. George

Background: Integrated primary care (IPC) is a final-year medical subject at the University of the Witwatersrand, Johannesburg, South Africa. It focusses on primary health care training. The coronavirus disease 2019 (COVID-19) pandemic exacerbated existing decentralised training challenges, including standardisation and patient exposure. This study explored IPC students’ experiences and perceptions of online learning during the COVID-19 pandemic. Methods: This explanatory-sequential mixed-methods study was informed by the technology acceptance model, community of inquiry model and self-regulated learning theory. A cross-sectional online survey was followed by focus group discussions (FGDs) (n = 2 and n = 3, respectively). All 316 medical students in the 2021 cohort were eligible to participate. Closed-ended survey responses were analysed using descriptive and inferential statistics. Open-ended responses were analysed using content analysis. The FGDs were thematically analysed. Results: The survey response rate was 52% (n = 164/316). Most students found the online content easily accessible (93.3%) and logically organised (80.0%). The course structure and organisation, and the range of online activities offered were the main features that supported learning. The main challenges included the content not being comprehensive and the difficulty of learning patient management from online content. Suggested improvements related to the course design and ways students and instructors can maximise the affordances of the online course. Conclusion: Acknowledging the limitations of learning clinical content online, the participants felt the course supported their learning. Our findings suggest that well-designed online content can augment clinical learning. Contribution: This study contributes to the discourse on the value of online learning for clinical teaching.

Eric Asempah PhD, Ene Ikpebe PhD, Michelle Wyndham-West PhD et al.

Introduction Cervical cancer constitutes a critical public health challenge in Ghana, with high morbidity and mortality despite the global availability of effective prophylactic Human Papillomavirus (HPV) vaccines. This study examines the policy discourse surrounding the implementation of a nationwide HPV vaccination program in Ghana, analyzes stakeholders’ perspectives on programmatic promotion, and assesses the extent of institutional prioritization. Methods Eight key informant interviews were thematically analyzed using NVivo; and a cross-sectional online survey of 215 participants was descriptively analyzed using SPSS. Results Thematic analysis of interviews revealed core policy challenges: weak prioritization, inadequate resource allocation, and policy framings that lacked discourse on the right to health. Survey data demonstrated marked improvement in HPV awareness (76.6%) and substantial interest in vaccination (64.2%), suggesting a shifting public health landscape influenced by media engagement and growing health literacy. Conclusion Findings underscore insufficient prioritization stalled the institutionalization of a national cervical cancer prevention strategy creating a critical implementation gap. However, the relatively late average age of sexual debut offers a strategic window for effective HPV vaccine delivery. Importantly, the convergence of increased public awareness, heightened receptivity to vaccination, and the availability of external funding mechanisms, such as support from Gavi, presents a timely and actionable opportunity for policy advancement. This study highlights the imperative for renewed governmental commitment to cervical cancer prevention, emphasizing the imperative to operationalize HPV vaccination as a core component of Ghana’s public health infrastructure.

Minghui Xu, Hao Fei, Fei Li et al.

Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which is challenging due to the lengthy and background information-rich na ture of the texts. In this work, We propose using a unified semantic discourse structure (S3) to represent document semantics, achieved by combining document-level rhetorical structure theory (RST) trees with sentence-level abstract meaning representation (AMR) graphs to construct S3 graphs. The hierarchical composition of sentence, clause, and word intrinsically characterizes the semantic meaning of the overall document. We then develop a headline generation framework, in which the S3 graphs are encoded as contextual features. To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph. Experimental results on two headline generation datasets demonstrate that our method outperforms existing state-of-art methods consistently. Our work can be instructive for a broad range of document modeling tasks, more than headline or summarization generation.

Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi et al.

Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains. The field of patent analysis and innovation is not an exception, where natural language processing (NLP) techniques presents opportunities to streamline and enhance important tasks -- such as patent classification and patent retrieval -- in the patent cycle. This not only accelerates the efficiency of patent researchers and applicants, but also opens new avenues for technological innovation and discovery. Our survey provides a comprehensive summary of recent NLP-based methods -- including multimodal ones -- in patent analysis. We also introduce a novel taxonomy for categorization based on tasks in the patent life cycle, as well as the specifics of the methods. This interdisciplinary survey aims to serve as a comprehensive resource for researchers and practitioners who work at the intersection of NLP, Multimodal AI, and patent analysis, as well as patent offices to build efficient patent systems.

Andrew Hoopes, Neel Dey, Victor Ion Butoi et al.

We present VoxelPrompt, an end-to-end image analysis agent that tackles free-form radiological tasks. Given any number of volumetric medical images and a natural language prompt, VoxelPrompt integrates a language model that generates executable code to invoke a jointly-trained, adaptable vision network. This code further carries out analytical steps to address practical quantitative aims, such as measuring the growth of a tumor across visits. The pipelines generated by VoxelPrompt automate analyses that currently require practitioners to painstakingly combine multiple specialized vision and statistical tools. We evaluate VoxelPrompt using diverse neuroimaging tasks and show that it can delineate hundreds of anatomical and pathological features, measure complex morphological properties, and perform open-language analysis of lesion characteristics. VoxelPrompt performs these objectives with an accuracy similar to that of specialist single-task models for image analysis, while facilitating a broad range of compositional biomedical workflows.

Bram Willemsen, Gabriel Skantze

We propose an approach to referring expression generation (REG) in visually grounded dialogue that is meant to produce referring expressions (REs) that are both discriminative and discourse-appropriate. Our method constitutes a two-stage process. First, we model REG as a text- and image-conditioned next-token prediction task. REs are autoregressively generated based on their preceding linguistic context and a visual representation of the referent. Second, we propose the use of discourse-aware comprehension guiding as part of a generate-and-rerank strategy through which candidate REs generated with our REG model are reranked based on their discourse-dependent discriminatory power. Results from our human evaluation indicate that our proposed two-stage approach is effective in producing discriminative REs, with higher performance in terms of text-image retrieval accuracy for reranked REs compared to those generated using greedy decoding.

Arun Govind Neelan

Von Neumann stability analysis, a well-known Fourier-based method, is a widely used technique for assessing stability in numerical computations. However, as noted in "Numerical Solution of Partial Differential Equations: Finite Difference Methods" by Smith (1985, pp. 67-68), this approach faces limitations when applied to multi-level methods employing schemes with more than two levels. In this study, we aim to extend the applicability of Von Neumann stability analysis to multi-level methods. An alternative method closely related to Von Neumann stability analysis is the Approximate Dispersion Relation (ADR) analysis. In this work, we not only explore ADR analysis but also introduce various ADR analysis variants while examining their inherent limitations so that other researchers can improve the analysis before using that in their work. Furthermore, we propose an innovative strategy for reducing dissipation, optimizing it through the use of an evolutionary algorithm. Our findings demonstrate that our proposed method yields minimal errors when compared to other advection equation schemes, both in one and two spatial dimensions.

Shengyi Hua, Fang Yan, Tianle Shen et al.

Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.

Kirill Solovev, Nicolas Pröllochs

Social media has become an indispensable channel for political communication. However, the political discourse is increasingly characterized by hate speech, which affects not only the reputation of individual politicians but also the functioning of society at large. In this work, we empirically analyze how the amount of hate speech in replies to posts from politicians on Twitter depends on personal characteristics, such as their party affiliation, gender, and ethnicity. For this purpose, we employ Twitter's Historical API to collect every tweet posted by members of the 117th U.S. Congress for an observation period of more than six months. Additionally, we gather replies for each tweet and use machine learning to predict the amount of hate speech they embed. Subsequently, we implement hierarchical regression models to analyze whether politicians with certain characteristics receive more hate speech. We find that tweets are particularly likely to receive hate speech in replies if they are authored by (i) persons of color from the Democratic party, (ii) white Republicans, and (iii) women. Furthermore, our analysis reveals that more negative sentiment (in the source tweet) is associated with more hate speech (in replies). However, the association varies across parties: negative sentiment attracts more hate speech for Democrats (vs. Republicans). Altogether, our empirical findings imply significant differences in how politicians are treated on social media depending on their party affiliation, gender, and ethnicity.

Maxwell Weinzierl, Sanda Harabagiu

Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel neural architecture, where the stance towards misinformation is organized in a knowledge graph. This new neural framework is enabling the identification of stance towards misinformation about COVID-19 vaccines with state-of-the-art results. The experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse. Because CoVaxLies provides a taxonomy of the misinformation about COVID-19 vaccines, we are able to show which type of misinformation is mostly adopted and which is mostly rejected.

Walter Giordano

The contribution of this study is expressed in the following research question: is it possible to identify some implicit-explicit textual or visual elements that catalyze or distract audience attention on or from the advertised product? An analysis of a corpus of advertisements taken from the Instagram pages of the major companies operating in the US food and beverage market reveals that attracting or distracting attention on or from a product’s use or function is a frequent strategy. These elements may contribute to changing scripts in advertising and mental patterns in consumer perceptions. The items that signal the distraction or the catalyzation of consumer attention in the advertising message have been identified via the ARCO model (St.Amant forth., St.Amant 2022), based upon the concept of usability of products, which helps decode the process of recognition of a product in the consumer’s brain. Results are promising, as this theory may help advertisers make more effective moves in the dual, cooperative relationship with consumers and review promotional strategies to engage the audience in advertising communication.

Vera V. Boguslavskaya, Hongbo Yu

Background. In connection with the active interest of researchers in the problems of social interaction in the public communicative space, it is reasonable for the authors to refer to the advertising technical discourse, where the features of social communication are due to different types of target audience, i.e., specialists, business people and the mass audience. The purpose of the study is to identify and characterize the sociolinguistic features of advertising technical discourse, taking into account the social purposefulness and targeting of this type of discourse. Materials and methods. The empirical material was advertising booklets and ads in Yandex from 2018 to 2022. The sample size for the article was 140 texts of technical advertising. When analyzing the material, interpretive analysis, the method of content analysis were used. Results. The results of the study showed that technical advertising texts intended for specialists are characterized by the use of special terms. Those addressed to business people are characterized by the dominance of lexical units of economic subjects, the active use of attributive phrases, abbreviations in the field of economics and the international security standard. Technical advertising for a mass audience is distinguished by the use of evaluative words and expressions, various stylistic means. The analysis of social interaction in advertising technical discourse made it possible to identify the sociolinguistic specifics of advertising technical discourse by characterizing the language means used in advertising created for different types of recipients. Practical implications. The results of the study can be used in practical work by advertising specialists who promote technical and high-tech products on the market; in lecture courses on lexicology and advertising discourse, they can also be useful to scientists involved in research on technical and scientific and technical advertising.

David A. Delaine, Julia Thompson

Partnerships are a central part of the service-learning experience. Recent research has focused on partnerships, types of interactions, and relationships across service-learning from various perspectives, yet examinations of service-learning partnerships from the student perspective, specifically the relationship between the community partner and the student, are limited. This investigation answers two research questions: (a) How do undergraduate engineering students characterize the nature of service-learning partnerships? and (b) What factors within service-learning influence student perspectives on partnerships? Focus groups and interviews were conducted with students who engaged in service-related engineering experiences, and the transcriptions of these discussions were analyzed using the transactional, cooperative, and communal framework, a method of thematic discourse analysis. Results revealed that the experiences of undergraduate students in service-learning provide valuable insight for analyzing partnerships, specifically with respect to the following themes: service component, social context, and community interactions. The factors shown to influence students’ perspectives of the service-learning partnership include the positionality of the students and the intentionality of the instructors. Our results imply that student experiences hold value beyond what is currently leveraged for research, teaching, and community outcomes. This study provides evidence that service-learning and community engagement efforts benefit when they are designed and evaluated in ways that acknowledge student voices and embrace students as knowledgeable and valuable members of university-community partnerships.

Gerrit Holtrup, William Lacube, Dimitri Percia David et al.

Fifth generation mobile networks (5G) are currently being deployed by mobile operators around the globe. 5G acts as an enabler for various use cases and also improves the security and privacy over 4G and previous network generations. However, as recent security research has revealed, the standard still has security weaknesses that may be exploitable by attackers. In addition, the migration from 4G to 5G systems is taking place by first deploying 5G solutions in a non-standalone (NSA) manner where the first step of the 5G deployment is restricted to the new radio aspects of 5G, while the control of the user equipment is still based on 4G protocols, i.e. the core network is still the legacy 4G evolved packet core (EPC) network. As a result, many security vulnerabilities of 4G networks are still present in current 5G deployments. This paper presents a systematic risk analysis of standalone and non-standalone 5G networks. We first describe an overview of the 5G system specification and the new security features of 5G compared to 4G. Then, we define possible threats according to the STRIDE threat classification model and derive a risk matrix based on the likelihood and impact of 12 threat scenarios that affect the radio access and the network core. Finally, we discuss possible mitigations and security controls. Our analysis is generic and does not account for the specifics of particular 5G network vendors or operators. Further work is required to understand the security vulnerabilities and risks of specific 5G implementations and deployments.