In this innovative book, a well-known feminist and sociologist-who is also the founding editor of Gender & Society-challenges our most basic assumptions about gender. Judith Lorber argues that gender is wholly a product of socialization, subject to human agency, organization, and interpretation, and that it is a social institution comparable to the economy, the family, and religion in its significance and consequences. Calling into question the inevitability and necessity of gender, she envisions a society structured for equality, where no gender, racial ethnic, or social class group is allowed to monopolize positions of power.
Bhaskara Hanuma Vedula, Darshan Anghan, Ishita Goyal
et al.
Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly. Existing benchmarks use name based proxies to detect implicit biases, which carry weak associations with many social demographics and cannot extend to dimensions like age or socioeconomic status. We introduce ImplicitBBQ, a QA benchmark that evaluates implicit bias through characteristic based cues, culturally associated attributes that signal implicitly, across age, gender, region, religion, caste, and socioeconomic status. Evaluating 11 models, we find that implicit bias in ambiguous contexts is over six times higher than explicit bias in open weight models. Safety prompting and chain-of-thought reasoning fail to substantially close this gap; even few-shot prompting, which reduces implicit bias by 84%, leaves caste bias at four times the level of any other dimension. These findings indicate that current alignment and prompting strategies address the surface of bias evaluation while leaving culturally grounded stereotypic associations largely unresolved. We publicly release our code and dataset for model providers and researchers to benchmark potential mitigation techniques.
Meysam Shirdel Bilehsavar, Negin Mahmoudi, Mohammad Jalili Torkamani
et al.
Sentiment analysis focuses on identifying the emotional polarity expressed in textual data, typically categorized as positive, negative, or neutral. Hate speech detection, on the other hand, aims to recognize content that incites violence, discrimination, or hostility toward individuals or groups based on attributes such as race, gender, sexual orientation, or religion. Both tasks play a critical role in online content moderation by enabling the detection and mitigation of harmful or offensive material, thereby contributing to safer digital environments. In this study, we examine the performance of three transformer-based models: BERT-base-multilingual-cased, RoBERTa-base, and XLM-RoBERTa-base with the first eight layers frozen, for multilingual sentiment analysis and hate speech detection. The evaluation is conducted across five languages: English, Korean, Japanese, Chinese, and French. The models are compared using standard performance metrics, including accuracy, precision, recall, and F1-score. To enhance model interpretability and provide deeper insight into prediction behavior, we integrate the Local Interpretable Model-agnostic Explanations (LIME) framework, which highlights the contribution of individual words to the models decisions. By combining state-of-the-art transformer architectures with explainability techniques, this work aims to improve both the effectiveness and transparency of multilingual sentiment analysis and hate speech detection systems.
Jacksaint Saintila, Laura E. Baquedano Santana, Salomón Huancahuire-Vega
et al.
Introduction:
Research on religiosity and healthy behaviors has been conducted mainly in developed countries; however, the Latin American context remains unexplored. This study examined the association between religiosity and healthy behaviors in members of the Seventh-day Adventist (SDA) church in different regions of Peru.
Methods:
A cross-sectional online survey was conducted between June and November 2022. The sample included 767 members of the SDA Church. Information was collected on sociodemographic characteristics and healthy behaviors such as physical activity, adequate sleep, among others. The Duke University Religion Index (DUREL) was used to measure religiosity. Binary logistic regression analyses were used to assess the association between DUREL subscales and healthy behaviors. Women represented 59.7% (n = 458) of the sample.
Results:
Higher scores on the religiosity subscales (organized religiosity [ORA], non-ORA, and intrinsic religiosity [IR]) were associated with >20 years of baptism in the SDA Church, receiving a pastoral visit ≥3 times during the past year, performing physical activity 3 to 4 times and ≥5 times/week, sleeping 7 to 9 h/day, attend training on healthy eating, healthy lifestyle seminars, and on 8 health practices promoted by the SDA Church. High IR was less likely in men.
Conclusion:
The findings of this study show that religiosity has a strong association with healthy behaviors, and that this relationship suggests that religiosity could be considered as a predictor of healthy behaviors.
Minority college students face unique challenges shaped by their identities based on their gender/sexual orientation, race, religion, and academic institutions, which influence their academic and social experiences. Although research has highlighted the challenges faced by individual minority groups, the stigma process-labeling, stereotyping, separation, status loss, and discrimination-that underpin these experiences remains underexamined, particularly in the online spaces where college students are highly active. We address these gaps by examining posts on subreddit, r/college, as indicators for stigma processes, our approach applies a Stereotype-BERT model, including stance toward each stereotype. We extend the stereotype model to encompass status loss and discrimination by using semantic distance with their reference sentences. Our analyses show that professional indicated posts are primarily labeled under the stereotyping stage, whereas posts indicating racial are highly represented in status loss and discrimination. Intersectional identified posts are more frequently associated with status loss and discrimination. The findings of this study highlight the need for multifaceted intersectional approaches to identifying stigma, which subsequently serve as indicators to promote equity for minority groups, especially racial minorities and those experiencing compounded vulnerabilities due to intersecting identities.
Azizul Hakim Fayaz, MD. Shorif Uddin, Rayhan Uddin Bhuiyan
et al.
Hate speech on digital platforms has become a growing concern globally, especially in linguistically diverse countries like Bangladesh, where regional dialects play a major role in everyday communication. Despite progress in hate speech detection for standard Bangla, Existing datasets and systems fail to address the informal and culturally rich expressions found in dialects such as Barishal, Noakhali, and Chittagong. This oversight results in limited detection capability and biased moderation, leaving large sections of harmful content unaccounted for. To address this gap, this study introduces BIDWESH, the first multi-dialectal Bangla hate speech dataset, constructed by translating and annotating 9,183 instances from the BD-SHS corpus into three major regional dialects. Each entry was manually verified and labeled for hate presence, type (slander, gender, religion, call to violence), and target group (individual, male, female, group), ensuring linguistic and contextual accuracy. The resulting dataset provides a linguistically rich, balanced, and inclusive resource for advancing hate speech detection in Bangla. BIDWESH lays the groundwork for the development of dialect-sensitive NLP tools and contributes significantly to equitable and context-aware content moderation in low-resource language settings.
Research on religion and migration has often focused on institutions and belief systems, while overlooking how mediation links migrants, sacred objects, rituals, and religious imaginaries. This study advances mediation as a core analytic in religion–migration studies by examining the practices of ten Thai migrant students in South Korea through semi-structured interviews on Buddhist amulets, Hindu deity pendants, Catholic rosaries, merit-making, and the elevation of sacred objects. Guided by Meyer’s religion-as-mediation framework and Taylor’s concept of the social imaginary, the analysis shows that quotidian, embodied engagements with sacred objects mediate and materialize Thai Buddhist–Animist imaginaries in Korean settings, expanding, transnationalizing, and hybridizing them through encounters with the host environment. These practices not only sustain spiritual continuity, but also generate sacred transnational social spaces that bridge both the ontological divide between the human and the transcendent and the geographical divide between Thailand and Korea. Rather than being preserved through institutional affiliation, migrant religiosity is continually reconstituted through everyday embodied practices of mediation that render the sacred experientially real in the host society. By foregrounding mediation, this study offers a reconceptualization of migrant religion as an embodied, material, and world-making process—one through which migrants actively reimagine and inhabit sacred spaces across borders.
This paper presents a systematic analysis of biases in open-source Large Language Models (LLMs), across gender, religion, and race. Our study evaluates bias in smaller-scale Llama and Gemma models using the SALT ($\textbf{S}$ocial $\textbf{A}$ppropriateness in $\textbf{L}$LM-Generated $\textbf{T}$ext) dataset, which incorporates five distinct bias triggers: General Debate, Positioned Debate, Career Advice, Problem Solving, and CV Generation. To quantify bias, we measure win rates in General Debate and the assignment of negative roles in Positioned Debate. For real-world use cases, such as Career Advice, Problem Solving, and CV Generation, we anonymize the outputs to remove explicit demographic identifiers and use DeepSeek-R1 as an automated evaluator. We also address inherent biases in LLM-based evaluation, including evaluation bias, positional bias, and length bias, and validate our results through human evaluations. Our findings reveal consistent polarization across models, with certain demographic groups receiving systematically favorable or unfavorable treatment. By introducing SALT, we provide a comprehensive benchmark for bias analysis and underscore the need for robust bias mitigation strategies in the development of equitable AI systems.
Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm. As the impact of these foundation models grows, understanding and evaluating their biases becomes crucial to achieving fairness and accuracy. We propose to study what LLMs know about the world we live in through the lens of geography. This approach is particularly powerful as there is ground truth for the numerous aspects of human life that are meaningfully projected onto geographic space such as culture, race, language, politics, and religion. We show various problematic geographic biases, which we define as systemic errors in geospatial predictions. Initially, we demonstrate that LLMs are capable of making accurate zero-shot geospatial predictions in the form of ratings that show strong monotonic correlation with ground truth (Spearman's $ρ$ of up to 0.89). We then show that LLMs exhibit common biases across a range of objective and subjective topics. In particular, LLMs are clearly biased against locations with lower socioeconomic conditions (e.g. most of Africa) on a variety of sensitive subjective topics such as attractiveness, morality, and intelligence (Spearman's $ρ$ of up to 0.70). Finally, we introduce a bias score to quantify this and find that there is significant variation in the magnitude of bias across existing LLMs. Code is available on the project website: https://rohinmanvi.github.io/GeoLLM
Location data privacy has become a serious concern for users as Location Based Services (LBSs) have become an important part of their life. It is possible for malicious parties having access to geolocation data to learn sensitive information about the user such as religion or political views. Location Privacy Preserving Mechanisms (LPPMs) have been proposed by previous works to ensure the privacy of the shared data while allowing the users to use LBSs. But there is no clear view of which mechanism to use according to the scenario in which the user makes use of a LBS. The scenario is the way the user is using a LBS (frequency of reports, number of reports). In this paper, we study the sensitivity of LPPMs on the scenario on which they are used. We propose a framework to systematically evaluate LPPMs by considering an exhaustive combination of LPPMs, attacks and metrics. Using our framework we compare a selection of LPPMs including an improved mechanism that we introduce. By evaluating over a variety of scenarios, we find that the efficacy (privacy, utility, and robustness) of the studied mechanisms is dependent on the scenario: for example the privacy of Planar Laplace geo-indistinguishability is greatly reduced in a continuous scenario. We show that the scenario is essential to consider when choosing an obfuscation mechanism for a given application.
Identity spans multiple dimensions; however, the relative salience of a dimension of identity can vary markedly from person to person. Furthermore, there is often a difference between one's internal identity (how salient different aspects of one's identity are to oneself) and external identity (how salient different aspects are to the external world). We attempt to capture the internal and external saliences of different dimensions of identity for influential users ("influencers") on Twitter using the follow graph. We consider an influencer's "ego-centric" profile, which is determined by their personal following patterns and is largely in their direct control, and their "audience-centric" profile, which is determined by the following patterns of their audience and is outside of their direct control. Using these following patterns we calculate a corresponding salience metric that quantifies how important a certain dimension of identity is to an individual. We find that relative to their audiences, influencers exhibit more salience in race in their ego-centric profiles and less in religion and politics. One practical application of these findings is to identify "bridging" influencers that can connect their sizeable audiences to people from traditionally underheard communities. This could potentially increase the diversity of views audiences are exposed to through a trusted conduit (i.e. an influencer they already follow) and may lead to a greater voice for influencers from communities of color or women.
In the 1640’s Elisabeth of Bohemia and René Descartes engaged in a philosophically rich correspondence. The most well-known aspect of the correspondence begins with a question Elisabeth asks Descartes about his account of the interaction between soul and body. This objection, often called the ‘problem of interaction’, has received much attention in contemporary scholarship and this attention frequently focuses on the exchange between Elisabeth and Descartes. Following the lead of Descartes himself, the majority of scholars treat the problem of interaction as the core, or even the entirety, of Elisabeth’s objections to Descartes from this stage in the correspondence. In this paper I argue that the driving force of Elisabeth’s objections to Descartes’ account is not the problem of interaction. Rather, Elisabeth’s objections to Descartes fundamentally concern Descartes’ account of the nature of the soul. While Elisabeth clearly offers the problem of interaction, it is only one of several worries each of which is designed to show that Descartes’ account of the soul is insufficient. I argue that Elisabeth raises three distinct problems to Descartes’ account of the nature of the soul: the causal interface problem, the vapors problem and the principal attribute problem.
Muhammad Fahroni Hamsan, Isman Isman, Imron Rosyadi
et al.
It is very important for a Muslim in everything to be guided or pay attention to Islamic rules so as not to fall into things that are prohibited by religion. Including in economic activities should pay attention to the rules in muamalah fiqh. The purpose of writing this article is to explain to the public what should not be done when someone is doing activities in developing property. The method used in this writing is the library research method with a qualitative descriptive approach. The result: among the rules of Islamic economics in the development of wealth is that buying and selling should not contain things that are prohibited or detrimental such as usury, fraud, theft and so on.
With the increase in the time spent at home, social media platforms' role has become an integral part of the public discussion in the COVID-19 period. Individuals use social media platforms to express their emotions, interact, and engage in public debate. Therefore, it is essential to analyze social media platforms for those wanting to understand public opinion during the pandemic. This thesis is the first study that examines the Turkish Twitter-sphere to understand the change in public opinion during the COVID-19 outbreak. For that purpose, starting from 12 February 2020 (one month before the first announced coronavirus cases in Turkey), 4.3 million Turkish tweets with a broad range of keywords are collected until June 2020 to investigate the public opinion change on different topics and to examine the actors leading to that change. The scope of the analysis is not only health-related discussion but also includes a broader range of themes such as politics, economy, and disinformation. This study also collects 4.15 million Turkish tweets with keywords of vaccine ("aşı" in Turkish) from 4 April 2020 until 17 March 2021 to unpack the health of the information ecosystem. Preliminary results suggest that (i) religion is the prominent phenomenon in Turkish people's perception of the pandemic, (ii) and the Turkish Twitter-sphere is highly vulnerable to mis/disinformation operations, and (iii) several communities with divergent interests exist in the vaccine network.