Neil Fernandes, Cheng Tang, Tehniyat Shahbaz
et al.
Community literacy programs supporting young newcomer children in Canada face limited staffing and scarce one-to-one time, which constrains personalized English and cultural learning support. This paper reports on a co-design study with United for Literacy tutors that informed Maple, a table-top, peer-like Socially Assistive Robot (SAR) designed as a practice partner within tutor-mediated sessions. From shadowing and co-design interviews, we derived newcomer-specific requirements and added them in an integrated prototype that uses short story-based activities, multi-modal scaffolding and embedded quizzes that support attention while producing tutor-actionable formative signals. We contribute system design implications for tutor-in-the-loop SARs supporting language socialization in community settings and outline directions for child-centered evaluation in authentic programs.
Large language models (LLMs) achieve strong performance on many tasks, but their progress remains uneven across languages and cultures, often reflecting values latent in English-centric training data. To enable practical cultural alignment, we propose a scalable approach that leverages national social studies curricula as a foundation for culture-aware supervision. We introduce CuCu, an automated multi-agent LLM framework that transforms national textbook curricula into open-ended, culture-specific question-answer pairs. Applying CuCu to the Korean national social studies curriculum, we construct KCaQA, comprising 34.1k open-ended QA pairs. Our quantitative and qualitative analyses suggest that KCaQA covers culture-specific topics and produces responses grounded in local sociocultural contexts.
How can we not adhere to the values proposed by the Ascona Charter? Has transforming anthropology in order to transform the world never been more urgent?
Anti-Jewish sentiment remains a global concern, often linked to religious interpretations. In Indonesia, despite the small number of Jews, their presence has become more visible, though they continue to face limitations due to the lack of state recognition and Islamic hegemony. The Qur’an contains numerous verses referencing Jews, some of which are perceived as fostering negative stereotypes. This article critically examines Hamka’s Tafsir al-Azhar, using a textual-analytical approach to explore his interpretation of these verses. Hamka portrays Jews as historically deceitful and hostile toward Islam, but he also warns against generalizing such traits to all Jews. Notably, his exegesis echoes themes of Zionist conspiracy, suggesting Jewish control over global finance and politics. While reflecting his sociopolitical context, Hamka’s commentary raises important questions about religious interpretation, prejudice, and the boundaries of Islamic exclusivism.
Eric J. W. Orlowski, Hakim Norhashim, Tristan Koh Ly Wey
While cultural alignment has increasingly become a focal point within AI research, current approaches relying predominantly on quantitative benchmarks and simplistic proxies fail to capture the deeply nuanced and context-dependent nature of human cultures. Existing alignment practices typically reduce culture to static demographic categories or superficial cultural facts, thereby sidestepping critical questions about what it truly means to be culturally aligned. This paper argues for a fundamental shift towards integrating interpretive qualitative approaches drawn from social sciences into AI alignment practices, specifically in the context of Large Language Models (LLMs). Drawing inspiration from Clifford Geertz's concept of "thick description," we propose that AI systems must produce outputs that reflect deeper cultural meanings--what we term "thick outputs"-grounded firmly in user-provided context and intent. We outline three necessary conditions for successful cultural alignment: sufficiently scoped cultural representations, the capacity for nuanced outputs, and the anchoring of outputs in the cultural contexts implied within prompts. Finally, we call for cross-disciplinary collaboration and the adoption of qualitative, ethnographic evaluation methods as vital steps toward developing AI systems that are genuinely culturally sensitive, ethically responsible, and reflective of human complexity.
The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answer questions that treat culture as isolated trivia. Such methods neglect the pluralistic and interactive realities of culture, and overlook how cultural assumptions permeate even ostensibly ``neutral'' evaluation settings. In this position paper, we argue for \textbf{intentionally cultural evaluation}: an approach that systematically examines the cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. We systematically characterize the what, how, and circumstances by which culturally contingent considerations arise in evaluation, and emphasize the importance of researcher positionality for fostering inclusive, culturally aligned NLP research. Finally, we discuss implications and future directions for moving beyond current benchmarking practices, discovering important applications that we don't know exist, and involving communities in evaluation design through HCI-inspired participatory methodologies.
The construction of railways made the mountains accessible to urban society in the age of industrialisation. The construction of the world’s first high mountain railway, the Semmering Railway, was followed by the development of Alpine summer resorts for tourists, very close to Vienna. Texts of the two Austrian writers Peter Rosegger (1843–1918) and Heimito von Doderer (1896–1966) offer the op- portunity to approach the people and their relationship to the mountains and the Semmering Railway. The analysis explores the questions: How did Rosegger experience the development of the mountains by the railway? How do Rosegger and Doderer’s travellers on the train experience the mountains? How do Rosegger and Doderer describe the people who came to Semmering because of the railway?
Rosegger impressively describes his first journey on the Semmering Railway, which opened the door to the world for him. He highlights the importance of observing nature from the train. He is critical of the development of the tourist settlements on the Semmering. In the texts by Rosegger and Doderer, the travellers on the train provide different insights into the fascination that emanates from the mountains. The urban society that arrived at the Semmering with the railway felt itself to have come closer to the mountains; the locals probably always perceived them as urban people.
Ethnology. Social and cultural anthropology, Arts in general
Multiple sclerosis (MS) is a disease affecting the brain and the spinal cord, in which the immune system attacks the myelin that protects nerve fibres, thus causing permanent damage resulting in various types of disabilities. As a (to date) incurable disease, the experience of the patient becomes central in coping with the many symptoms, especially in the way it is communicated, not only to treating physicians, but also to society at large. Over the last years MS, which a century ago seemed to affect women and men alike, has shown an increasing prevalence in the female to male ratio, both in small cohorts (Kotzamani et al., 2012; Krökki et al. 2011), and worldwide (Sellner et al., 2011). In view of this tendency, the study of MS has begun to include a gender approach, focusing on the potential explanatory factors, but also on the specific circumstances affecting women (Jobin et al., 2010). In our paper, drawing from a sample extracted from online testimonials, an analysis will be carried out of the various metaphorical imagery used by women to explain their symptoms to doctors, relatives, and society at large. Following the traditional classification by Lakoff and Johnson (1980: 14), we shall explore the framings used, which may lead to positive or negative experiences of the disease and may have an empowering potential when patients «fight» MS in the general framework of managing the condition.
Large language models (LLMs) face challenges in aligning with diverse cultural values despite their remarkable performance in generation, which stems from inherent monocultural biases and difficulties in capturing nuanced cultural semantics. Existing methods struggle to adapt to unknown culture after fine-tuning. Inspired by cultural geography across five continents, we propose Cultural Palette, a multi-agent framework that redefines cultural alignment as an adaptive "color-blending" process for country-specific adaptation. Our approach harnesses cultural geography across five continents through three key steps: First, we synthesize the Pentachromatic Cultural Palette Dataset using GPT-4o, refining continental-level dialogues with Hofstede's cultural dimensions to establish foundational cultural representations. Second, five continent-level alignment agents form specialized cultural communities that generate region-specific draft responses. Third, a Meta Agent employs Cultural MoErges to dynamically blend these cultural "colors" through attention-gated parameter merging, akin to mixing pigments on a palette, resolving conflicts while preserving cultural nuances to produce the final culturally-aligned response. Extensive experiments across various countries demonstrate that \textit{Cultural Palette} surpasses existing baselines in cultural alignment.
LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.
The detection of depression in social media posts is crucial due to the increasing prevalence of mental health issues. Traditional machine learning algorithms often fail to capture intricate textual patterns, limiting their effectiveness in identifying depression. Existing studies have explored various approaches to this problem but often fall short in terms of accuracy and robustness. To address these limitations, this research proposes a neural network architecture leveraging transformer-based models combined with metadata and linguistic markers. The study employs DistilBERT, extracting information from the last four layers of the transformer, applying learned weights, and averaging them to create a rich representation of the input text. This representation, augmented by metadata and linguistic markers, enhances the model's comprehension of each post. Dropout layers prevent overfitting, and a Multilayer Perceptron (MLP) is used for final classification. Data augmentation techniques, inspired by the Easy Data Augmentation (EDA) methods, are also employed to improve model performance. Using BERT, random insertion and substitution of phrases generate additional training data, focusing on balancing the dataset by augmenting underrepresented classes. The proposed model achieves weighted Precision, Recall, and F1-scores of 84.26%, 84.18%, and 84.15%, respectively. The augmentation techniques significantly enhance model performance, increasing the weighted F1-score from 72.59% to 84.15%.
Conversations often adhere to well-understood social norms that vary across cultures. For example, while "addressing parents by name" is commonplace in the West, it is rare in most Asian cultures. Adherence or violation of such norms often dictates the tenor of conversations. Humans are able to navigate social situations requiring cultural awareness quite adeptly. However, it is a hard task for NLP models. In this paper, we tackle this problem by introducing a "Cultural Context Schema" for conversations. It comprises (1) conversational information such as emotions, dialogue acts, etc., and (2) cultural information such as social norms, violations, etc. We generate ~110k social norm and violation descriptions for ~23k conversations from Chinese culture using LLMs. We refine them using automated verification strategies which are evaluated against culturally aware human judgements. We organize these descriptions into meaningful structures we call "Norm Concepts", using an interactive human-in-loop framework. We ground the norm concepts and the descriptions in conversations using symbolic annotation. Finally, we use the obtained dataset for downstream tasks such as emotion, sentiment, and dialogue act detection. We show that it significantly improves the empirical performance.
Sinem Getir Yaman, Ana Cavalcanti, Radu Calinescu
et al.
Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compliance with these norms, the rules they induce need to be unambiguously defined, checked for consistency, and used to verify the agent. In this paper, we introduce a framework for formal specification, validation and verification of social, legal, ethical, empathetic and cultural (SLEEC) rules for autonomous agents. Our framework comprises: (i) a language for specifying SLEEC rules and rule defeaters (that is, circumstances in which a rule does not apply or an alternative form of the rule is required); (ii) a formal semantics (defined in the process algebra tock-CSP) for the language; and (iii) methods for detecting conflicts and redundancy within a set of rules, and for verifying the compliance of an autonomous agent with such rules. We show the applicability of our framework for two autonomous agents from different domains: a firefighter UAV, and an assistive-dressing robot.
Using mass sources in historical research makes it possible to improve the representativeness of knowledge about the historical period. However, qualitative text analysis has limitations when working with a large array of data. Thus, in modern historical science is a need to involve computer («intellectual») analysis of texts for processing mass sources. Based on principles of the discourse-historical approach, authors indicated the possibility of using the RStudio computer program for intellectual analysis of the voluminous corpus of information reports and reviews of the GPU–OGPU of the 1920s on the materials of the North Caucasus. In the first stage, the contextualization of the studied sources was carried out. The authors found that reports and reviews of the GPU-OGPU of different years had similar terminological, emotive and thematic composition. While maintaining the internal unity of the OGPU information documents on the North Caucasus, an increase in the number of topics in summaries and reviews has been revealed since 1925. Emotional aggravation and complication of the «Chekist» discourse intensified so much by the end of the 1920s that terminological composition of the documents of 1928-1929 differed significantly from composition of texts of previous years. Thus, the text mining of the reports and reviews of the GPU–OGPU demonstrated the transformation of the regional «Chekist» discourse, which was broadcast to the top leadership of the country. The applied approach allows minimizing subjectivity of researcher’s assessments and conclusions.
Ethnology. Social and cultural anthropology, History of Russia. Soviet Union. Former Soviet Republics
Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions. However, children's development involve complex faculties such as exploration, creativity and curiosity which are challenging to model. Also, often children express themselves in a playful way which is different from a typical adult behaviour. Different children also have different needs, and it remains a challenge in the current state of the art that those of neurodiverse children are under-addressed. With this workshop, we aim to promote a common ground among different disciplines such as developmental sciences, artificial intelligence and social robotics and discuss cutting-edge research in the area of user modelling and adaptive systems for children.
Giulia Zaino, Carmine Tommaso Recchiuto, Antonio Sgorbissa
The article introduces the concept of image "culturization," which we define as the process of altering the ``brushstroke of cultural features" that make objects perceived as belonging to a given culture while preserving their functionalities. First, we defined a pipeline for translating objects' images from a source to a target cultural domain based on state-of-the-art Generative Adversarial Networks. Then, we gathered data through an online questionnaire to test four hypotheses concerning the impact of images belonging to different cultural domains on Italian participants. As expected, results depend on individual tastes and preferences: however, they align with our conjecture that some people, during the interaction with an intelligent system, will prefer to be shown images modified to match their cultural background. The study has two main limitations. First, we focussed on the culturization of individual objects instead of complete scenes. However, objects play a crucial role in conveying cultural meanings and can strongly influence how an image is perceived within a specific cultural context. Understanding and addressing object-level translation is a vital step toward achieving more comprehensive scene-level translation in future research. Second, we performed experiments with Italian participants only. We think that there are unique aspects of Italian culture that make it an interesting and relevant case study for exploring the impact of image culturization. Italy is a very culturally conservative society, and Italians have specific sensitivities and expectations regarding the accurate representation of their cultural identity and traditions, which can shape individuals' preferences and inclinations toward certain visual styles, aesthetics, and design choices. As a consequence, we think they are an ideal candidate for a preliminary investigation of image culturization.
AbstractThis study explores the Santhal community to enhance the understanding of the human-nature relationship that fully captures distinct intricacies of ethnoecology. Relying on a qualitative research design, this study focuses on the perception and interpretation of environmental aspects using ethnoscientific methods among Santhals in West Bengal, India. It reveals that Santhals are still unique in perceiving the environment learned through folk models. Santhal’s perception of environmental domains is constituted by various cognitive elements (resource distributions, care, feelings, attachment, myths, and superstitious credence toward their environment) and multifaceted interpretations (living beings, nonliving objects, natural and built environment). Based on its evidence, this study recommends that indigenous worldview-based ethnoscientific knowledge is the identity of indigenity that shapes ethnoscientific knowledge can be used in sustainable resource management practice. Furthermore, the study proposes a view that ignoring this unique ethnoscientific knowledge-based worldview base may degenerate the indigenous culture.
Целью статьи является философское обоснование необходимости введения в образовательные стандарты по экологии основ информационной экологии. Автор считает, что без этой дисциплины качественное экологическое образование, отвечающее реалиям цифровой эпохи, невозможно. Знание законов информационной экологии необходимо, чтобы не допустить негативного воздействия информационной среды на человека: манипулирования сознанием человека и зависимости от цифровой среды.
The current study intended to analyze the levels of joy in religious festivals. It involved 387 Pakistani Muslims including men (n=143) and women (n=282). Data was collected through a specifically developed questionnaire in Urdu. The findings revealed that Pakistani Muslims enjoy their festivals at a very low degree and the levels of joy in Eid-ul-Fitr and Eid-ul-Adha remains significantly lower than the levels of joy in weddings. The level of joy for Pakistanis during different festivals could not exceed 31 percent. The findings further revealed that, instead of being joyous, a little minority of the respondents felt sadness and tiredness while celebrating different festivals. Men had significantly higher levels of joy on Eid-Ul-Fitr and Eid-Ul-Adha as compared to women. Women had significantly higher levels of joy on close-relative’s wedding as compared to men. Unmarried had significantly higher levels of joy on friend’s wedding as compared to married.
Kajian kali ini bertujuan untuk menganalisis tingkat keceriaan pada hari raya keagamaan. Ini melibatkan 387 Muslim Pakistan termasuk pria (n=143) dan wanita (n=282). Data dikumpulkan melalui kuesioner yang dikembangkan secara khusus dalam bahasa Urdu. Temuan mengungkapkan bahwa Muslim Pakistan menikmati festival mereka pada tingkat yang sangat rendah dan tingkat kegembiraan di Idul Fitri dan Idul Adha tetap jauh lebih rendah daripada tingkat kegembiraan dalam pernikahan. Tingkat kegembiraan orang Pakistan selama festival yang berbeda tidak bisa melebihi 31 persen. Temuan lebih lanjut mengungkapkan bahwa, alih-alih gembira, sebagian kecil responden merasakan kesedihan dan kelelahan saat merayakan festival yang berbeda. Pria memiliki tingkat kegembiraan yang jauh lebih tinggi pada Idul Fitri dan Idul Adha dibandingkan dengan wanita. Wanita memiliki tingkat kegembiraan yang jauh lebih tinggi pada pernikahan kerabat dekat dibandingkan dengan pria. Belum menikah memiliki tingkat kegembiraan yang jauh lebih tinggi pada pernikahan teman dibandingkan dengan menikah.