Search Results (9)

The rise of algorithm-driven virtual museums presents a philosophical challenge for how cultural meaning is constructed and critiqued in digital curation. Prevailing approaches highlight important but partial aspects: the loss of aura and authenticity in digital reproductions, efforts to maintain semiotic continuity with physical exhibits, optimistic narratives of technological democratisation, and critical technopessimist warnings about commodification and bias. Yet none provides a unified theoretical model of meaning-making under algorithmic curation. This paper proposes a dialectical-semiotic framework to synthesise and transcend these positions. The Dialectical Modelling Nexus (DMN) is a new conceptual structure that views meaning in virtual museums as emerging from the dynamic interplay of original and reproduced contexts, human and algorithmic sign systems, personal interpretation, and ideological framing. Through a critique of prior theories and a synthesis of their insights, the DMN offers a comprehensive model to diagnose how algorithms mediate museum content and to guide critical curatorial practice. The framework illuminates the dialectical tensions at the heart of algorithmic cultural mediation and suggests principles for preserving authentic, multi-layered meaning in the digital museum milieu. Full article

In the context of changing demographics at regional universities (including our own), we highlight an ongoing project at our university that addresses the last area of acceptable bias in English-medium higher education: bias against speakers of other languages and non-standard dialects of English. We discuss the hegemonic aspect of the Standard Academic English used by default at most US institutions of higher education and its role in potential discrimination against users of languages other than English and dialects other than the Standard. Data from over 2000 surveys, 55 follow up interviews, and three focus groups from faculty, staff and students in the university community are being analyzed. Preliminary findings show pervasive ignorance of the nature of language variation and how that plays a role in continuing discrimination against those who use other languages and diverse varieties of English even in our very multilingual setting. We conclude by outlining next steps, including the development of onboarding materials for new faculty, staff, and students. Full article

The current study investigated the attitudes of 340 Saudi college students towards two Arabic dialectal variations, kaskasah and kaʃkaʃah, utilizing the matched-guise technique. Participants listened to recordings of a speaker using each variation and evaluated the speaker on various personality traits, regional origin, and hireability. The findings revealed generally positive attitudes towards both variations, with the majority associating the speaker with desirable traits such as humility, kindness, friendliness, and respectfulness. However, the kaskasah variation was perceived slightly more favorably overall compared to kaʃkaʃah. The study also found distinct regional associations, with kaskasah slightly more strongly linked to the Najdi dialect and kaʃkaʃah overwhelmingly associated with the Southern dialect. Notably, a considerable minority indicated that they would not hire speakers of these variations, particularly kaʃkaʃah, suggesting some degree of dialect-based bias. The study contributes to research on language attitudes in Saudi Arabia by highlighting the complex interplay between dialectal variation, regional identity, and social evaluation. The findings underscore the importance of promoting linguistic awareness and inclusivity to mitigate the negative effects of dialect-based stereotyping and bias. Full article

Moroccan Darija, a dialect of Arabic, presents unique challenges for natural language processing due to its lack of standardized orthographies, frequent code switching, and status as a low-resource language. In this work, we focus on detecting offensive language in Darija, addressing these complexities. We present three key contributions that advance the field. First, we introduce a human-labeled dataset of Darija text collected from social media platforms. Second, we explore and fine-tune various language models on the created dataset. This investigation identifies a Darija RoBERTa-based model as the most effective approach, with an accuracy of 90% and F1 score of 85%. Third, we evaluate the best model beyond accuracy by assessing properties such as correctness, robustness and fairness using metamorphic testing and adversarial attacks. The results highlight potential vulnerabilities in the model’s robustness, with the model being susceptible to attacks such as inserting dots (29.4% success rate), inserting spaces (24.5%), and modifying characters in words (18.3%). Fairness assessments show that while the model is generally fair, it still exhibits bias in specific cases, with a 7% success rate for attacks targeting entities typically subject to discrimination. The key finding is that relying solely on offline metrics such as the F1 score and accuracy in evaluating machine learning systems is insufficient. For low-resource languages, the recommendation is to focus on identifying and addressing domain-specific biases and enhancing pre-trained monolingual language models with diverse and noisier data to improve their robustness and generalization capabilities in diverse linguistic scenarios. Full article

The experiential bias in modern understandings of spirituality has led to readings of the pre-modern texts of Pseudo-Dionysius as referring to “negative experiences” of faith. Denys Turner, Bernard McGinn, and others have outlined the mistaken “spiritual positivism” of such readings and their contrast with the negative dialectics of the classical apophatic tradition. Indeed, the philosophical parameters of the Christian mysticism of the Dionysian tradition would deny “mystical experience” to be “experience” as such. Nevertheless, several modern theologians have attempted to integrate interpretations of the experiential in Christian mysticism into their theology. These include Sara Coakley in the idea of spiritual sense in her theology of the body, Karl Rahner in the conception of spiritual touch within his theology of grace, and Louis Dupré’s view that there is religious significance in the experience of “emptiness” in modern-day atheism. I shall contrast these attempted integrations with the critique of “mystical experience” within classical understandings of apophaticism. Full article

(1) Background: This systematic review presents an overview of psychological interventions in suicide published between 2013 and 2023 in Spain and Japan, sparked by Spain’s alarming recent increase in suicide rates and the potential exemplar of Japan’s reduction efforts. (2) Methods: Following the PRISMA checklist, the databases Web of Science, Scopus, PubMed, and PsycInfo were searched using the terms [(“suicide” OR “suicidal behavior” OR “suicidal attempt” OR “suicidal thought” OR “suicidal intention”) AND (“prevention” OR “intervention” OR “psychosocial treatment” OR “Dialectical Behavior Therapy” OR “Cognitive Therapy” OR “psychotherap*”)] AND [(“Spain” OR “Spanish”) OR (“Japan” OR “Japanese”)]. We included articles published in peer-reviewed academic journals, written in English, Spanish, and Japanese between 2013 and 2023 that presented, designed, implemented, or assessed psychological interventions focused on suicidal behavior. (3) Results: 46 studies were included, concerning prevention, treatment, and training interventions. The risk of bias was low in both Spanish and Japanese studies, despite the lack of randomization of the samples. We identified common characteristics, such as psychoeducation and coping skills. Assertive case management was only highlighted in Japan, making an emphasis on active patient involvement in his/her care plan. (4) Conclusions: The findings will help professionals to incorporate into their interventions broader, more comprehensive approaches to consider more interpersonal components. Full article

Understanding the public’s diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms. Full article

Digital innovations and interventions can potentially revolutionize agri-food systems, especially in coping with climate challenges. On a similar note, digital research tools and methods are increasingly popular for the efficient collection and analysis of real-time, large-scale data. It is claimed that these methods can also minimize subjective biases that are prevalent in traditional qualitative research. However, given the digital divide, especially affecting women and marginalized communities, these innovations could potentially introduce further disparities. To assess these contradictions, we piloted SenseMaker, a digital ethnography tool designed to capture individual, embodied experiences, biases, and perceptions to map vulnerabilities and resilience to climate impacts in the Gaya District in Bihar. Our research shows that this digital tool allows for a systematic co-design of the research framework, allows for the collection of large volumes of data in a relatively short time, and a co-analysis of the research data by the researchers and the researched. This process allowed us to map and capture the complexities of intersectional inequalities in relation to climate change vulnerability. However, we also noted that the application of the tool is influenced by the prior exposure to technology (digital devices) of both the enumerators and researched groups and requires significant resources when implemented in contexts where there is a need to translate the data from local dialects and languages to more dominant languages (English). Most importantly, perceptions, positionalities, and biases of researchers can significantly impact the design of the tool’s signification framework, reiterating the fact that researcher bias persists regardless of technological innovations in research methodology. Full article

The presence of laryngeal disease affects vocal fold(s) dynamics and thus causes changes in pitch, loudness, and other characteristics of the human voice. Many frameworks based on the acoustic analysis of speech signals have been created in recent years; however, they are evaluated on just one or two corpora and are not independent to voice illnesses and human bias. In this article, a unified wavelet-based paradigm for evaluating voice diseases is presented. This approach is independent of voice diseases, human bias, or dialect. The vocal folds’ dynamics are impacted by the voice disorder, and this further modifies the sound source. Therefore, inverse filtering is used to capture the modified voice source. Furthermore, the fundamental frequency independent statistical and energy metrics are derived from each spectral sub-band to characterize the retrieved voice source. Speech recordings of the sustained vowel /a/ were collected from four different datasets in German, Spanish, English, and Arabic to run the several intra and inter-dataset experiments. The classifiers’ achieved performance indicators show that energy and statistical features uncover vital information on a variety of clinical voices, and therefore the suggested approach can be used as a complementary means for the automatic medical assessment of voice diseases. Full article