Hasil untuk "Women. Feminism"

Dejene Tesfaye, Tadesse Misgana, Berhe Gebremichael et al.

IntroductionCommon mental disorders (CMD) cause marked emotional distress and interfere with daily function among postpartum women. In addition, the negative attitude towards mental disorders and their treatments prevents the women from seeking mental healthcare. Very little is known about CMD among women, especially in the postpartum period. This study, therefore, aimed at assessing the prevalence of CMD and its associated factors among women in the postpartum period at Kersa and Haramaya Health and Demographic Surveillance Sites (HDSS) sites, in eastern Ethiopia, between 1 July 2021 and 28 February 2022.MethodsThis study employed a community-based cross-sectional study design using a quantitative method and was conducted in Kersa and Haramaya HDSS among 1,016 postpartum women. A structured questionnaire was used to collect data through face-to-face interviews about the variables related to sociodemographic and economic, clinical, psychosocial, substance use, and obstetric complication characteristics. The CMD was assessed by using the Self-Reporting Questionnaire (SRQ-20). A bivariable and multivariable logistic regression analysis was performed. All the variables with a p-value <0.25 in bivariable logistic regression were taken to multivariable logistic regression. Variables with a p-value < 0.05 in the multivariable regression were declared statistically significant associations. The odds ratio (OR) and 95% confidence intervals (CI) were used to show the strength of the association.ResultThe prevalence of postpartum CMD was 23.84% (95% CI: 21.21–26.47). Among pregnant women who had CMD, only 103 (27.7%) had CMD and persisted to the postpartum period. Poor social support [adjusted OR (aOR): 1.88, 95% CI: 1.28–2.74], wealth index in the first quantile (aOR: 1.59, 95% CI: 1.06–2.39), presence of obstetric complication (aOR: 7.74, 95% CI: 4.38–13.69), and cesarean delivery (aOR: 5.01, 95% CI: 1.14–22.13) were the factors that showed a statistically significant association with postpartum CMD.ConclusionOne in every four study participants had CMD, which was higher than in studies conducted in Ethiopia among postpartum women. Social support, wealth index, obstetric complications, and mode of delivery were the factors with statistically significant associations. Postpartum women may benefit from the early diagnosis and treatment of CMD at the community and the primary healthcare level, and the integration of mental healthcare into maternal health services.

Mohammad Zia Ur Rehman, Sufyaan Zahoor, Areeb Manzoor et al.

A substantial portion of offensive content on social media is directed towards women. Since the approaches for general offensive content detection face a challenge in detecting misogynistic content, it requires solutions tailored to address offensive content against women. To this end, we propose a novel multimodal framework for the detection of misogynistic and sexist content. The framework comprises three modules: the Multimodal Attention module (MANM), the Graph-based Feature Reconstruction Module (GFRM), and the Content-specific Features Learning Module (CFLM). The MANM employs adaptive gating-based multimodal context-aware attention, enabling the model to focus on relevant visual and textual information and generating contextually relevant features. The GFRM module utilizes graphs to refine features within individual modalities, while the CFLM focuses on learning text and image-specific features such as toxicity features and caption features. Additionally, we curate a set of misogynous lexicons to compute the misogyny-specific lexicon score from the text. We apply test-time augmentation in feature space to better generalize the predictions on diverse inputs. The performance of the proposed approach has been evaluated on two multimodal datasets, MAMI and MMHS150K, with 11,000 and 13,494 samples, respectively. The proposed method demonstrates an average improvement of 10.17% and 8.88% in macro-F1 over existing methods on the MAMI and MMHS150K datasets, respectively.

Manel Rakez, Thomas Louis, Julien Guillaumin et al.

Risk-adapted breast cancer screening requires robust models that leverage longitudinal imaging data. Most current deep learning models use single or limited prior mammograms and lack adaptation for real-world settings marked by imbalanced outcome distribution and heterogeneous follow-up. We developed LongiMam, an end-to-end deep learning model that integrates both current and up to four prior mammograms. LongiMam combines a convolutional and a recurrent neural network to capture spatial and temporal patterns predictive of breast cancer. The model was trained and evaluated using a large, population-based screening dataset with disproportionate case-to-control ratio typical of clinical screening. Across several scenarios that varied in the number and composition of prior exams, LongiMam consistently improved prediction when prior mammograms were included. The addition of prior and current visits outperformed single-visit models, while priors alone performed less well, highlighting the importance of combining historical and recent information. Subgroup analyses confirmed the model's efficacy across key risk groups, including women with dense breasts and those aged 55 years or older. Moreover, the model performed best in women with observed changes in mammographic density over time. These findings demonstrate that longitudinal modeling enhances breast cancer prediction and support the use of repeated mammograms to refine risk stratification in screening programs. LongiMam is publicly available as open-source software.

David Giofrè, David C. Geary, Lewis G. Halsey

On average men are taller and more muscular than women, which confers on them advantages related to female choice and during physical competition with other men. Sexual size dimorphisms such as these come with vulnerabilities due to higher maintenance and developmental costs for the sex with the larger trait. These costs are in keeping with evolutionary theory that posits large, elaborate, sexually selected traits are signals of health and vitality because stressor exposure (e.g.\ early disease) will compromise them (e.g.\ shorter stature) more than other traits. We provide a large-scale test of this hypothesis for the human male and show that with cross-national and cross-generational improvements in living conditions, where environmental stressors recede, men's gains in height and weight are more than double those of women's, increasing sexual size dimorphism. Our study combines evolutionary biology with measures of human wellbeing, providing novel insights into how socio-ecological factors and sexual selection shape key physical traits.

Ashwin Rao, Sze Yuh Nina Wang, Kristina Lerman

The U.S. Supreme Court's 2022 ruling in Dobbs v. Jackson Women's Health Organization marked a turning point in the national debate over reproductive rights. While the ideological divide over abortion is well documented, less is known about how gender and local sociopolitical contexts interact to shape public discourse. Drawing on nearly 10 million abortion-related posts on X (formerly Twitter) from users with inferred gender, ideology and location, we show that gender significantly moderates abortion attitudes and emotional expression, particularly in conservative regions, and independently of ideology. This creates a gender gap in abortion attitudes that grows more pronounced in conservative regions. The leak of the Dobbs draft opinion further intensified online engagement, disproportionately mobilizing pro-abortion women in areas where access was under threat. These findings reveal that abortion discourse is not only ideologically polarized but also deeply structured by gender and place, highlighting the central role of identity in shaping political expression during moments of institutional disruption.

Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne et al.

Early detection of breast cancer is critical for improving patient outcomes. While mammography remains the primary screening modality, magnetic resonance imaging (MRI) is increasingly recommended as a supplemental tool for women with dense breast tissue and those at elevated risk. However, the acquisition and interpretation of multiparametric breast MRI are time-consuming and require specialized expertise, limiting scalability in clinical practice. Artificial intelligence (AI) methods have shown promise in supporting breast MRI interpretation, but their development is hindered by the limited availability of large, diverse, and publicly accessible datasets. To address this gap, we present a publicly available, multi-center breast MRI dataset collected across six clinical institutions in five European countries. The dataset comprises 741 examinations from women undergoing screening or diagnostic breast MRI and includes malignant, benign, and non-lesion cases. Data were acquired using heterogeneous scanners, field strengths, and acquisition protocols, reflecting real-world clinical variability. In addition, we report baseline benchmark experiments using a transformer-based model to illustrate potential use cases of the dataset and to provide reference performance for future methodological comparisons.

Renan Torres Alves

O presente trabalho tem como objetivo analisar como a mobilização do sintagma “ideologia de gênero” na Organização das Nações Unidos acarreta na reativação do direito natural como fundamento do Direito Internacional dos Direitos Humanos. Utiliza-se como marco teórico o trabalho desenvolvido pela autora Marie-Bénédicte Dembour (2005) a respeito das quatro escolas de pensamento em torno da teoria do Direito Internacional dos Direitos Humanos. Dessa forma, esse artigo, a partir de uma revisão bibliográfica e documental, busca localizar a reação anti-gênero no âmbito da ONU e os fundamentos jurídicos utilizados por seus autores para defendê-la. Com o presente trabalho busco reafirmar a importância de reflexão sobre as bases teóricas dos direitos humanos a fim de priorizar uma visão cada vez mais pluralista e democrática destes.

Nan Miles Xi, Hong-Long Ji, Lin Wang

Sarcoidosis is a rare inflammatory disease characterized by the formation of granulomas in various organs. The disease presents diagnostic and treatment challenges due to its diverse manifestations and unpredictable nature. In this study, we employed a Large Language Model (LLM) to analyze sarcoidosis-related discussions on the social media platform Reddit. Our findings underscore the efficacy of LLMs in accurately identifying sarcoidosis-related content. We discovered a wide array of symptoms reported by patients, with fatigue, swollen lymph nodes, and shortness of breath as the most prevalent. Prednisone was the most prescribed medication, while infliximab showed the highest effectiveness in improving prognoses. Notably, our analysis revealed disparities in prognosis based on age and gender, with women and younger patients experiencing good and polarized outcomes, respectively. Furthermore, unsupervised clustering identified three distinct patient subgroups (phenotypes) with unique symptom profiles, prognostic outcomes, and demographic distributions. Finally, sentiment analysis revealed a moderate negative impact on patients' mental health post-diagnosis, particularly among women and younger individuals. Our study represents the first application of LLMs to understand sarcoidosis through social media data. It contributes to understanding the disease by providing data-driven insights into its manifestations, treatments, prognoses, and impact on patients' lives. Our findings have direct implications for improving personalized treatment strategies and enhancing the quality of care for individuals living with sarcoidosis.

Cassandra Crowe, Belinda Middleweek, Laura Ryan et al.

We surveyed Australian finance professionals and tested whether there are statistically significant differences in promotional propensity according to gender identity. The findings indicate men and women are equally likely to ask for promotion, however, 'gifted advancements' account for the higher statistical frequency of promotions among men. These gender-based differences in behaviors have been overlooked in existing research on promotion. We call for a standardized framework for the development of promotion policies to address this industry-wide problem.

Carlota Álvarez Maylín

Luciana María di Leone

Llama la atención que, en el marco de los debates y las manifestaciones por la legalización del aborto que se produjeron en Buenos Aires, a lo largo de 2018, la poesía se haya afirmado como uno de los discursos presentes en las disputas de la plaza pública. Son muchos los ejemplos: en las redes sociales poniendo el tema en escena, en revistas, en libros autorales auto-gestionados, mencionada dentro del parlamento, en la fuerte movilización de las #poetasporelderechoalabortolegal, que se presentaban en los escenarios de la plaza y llegaron a publicar el libro Martes Verde. Entonces, ¿cuál es la relación entre la poesía y el debate sobre el aborto? ¿Qué regímenes de lectura o de escucha poética están implicados en las menciones hechas dentro de la Cámara y en la puesta en acto en la plaza? A partir del análisis de algunos poemas utilizados en los debates, este texto pretende observar cuál es la definición de poesía y de literatura que se disputó en paralelo a la disputa por la legalización del aborto.

Yu-Ru Lin, Shaomei Wu, Winter Mason

Literacy is one of the most fundamental skills for people to access and navigate today's digital environment. This work systematically studies the language literacy skills of online populations for more than 160 countries and regions across the world, including many low-resourced countries where official literacy data are particularly sparse. Leveraging public data on Facebook, we develop a population-level literacy estimate for the online population that is based on aggregated and de-identified public posts written by adult Facebook users globally, significantly improving both the coverage and resolution of existing literacy tracking data. We found that, on Facebook, women collectively show higher language literacy than men in many countries, but substantial gaps remain in Africa and Asia. Further, our analysis reveals a considerable regional gap within a country that is associated with multiple socio-technical inequalities, suggesting an "inequality paradox" -- where the online language skill disparity interacts with offline socioeconomic inequalities in complex ways. These findings have implications for global women's empowerment and socioeconomic inequalities.

Marina Susana Cendán Caaveiro

El objetivo de este estudio es repensar el surrealismo, contextualizándolo en el presente y revisando críticamente algunos de sus postulados teóricos, particularmente aquellos que han incidido en la construcción de una imagen estereotipada de la mujer. Musas, niñas eternas o femme fatales, los surrealistas ejercieron a través de sus obras y escritos una manipulación continuada y, en muchos casos, intolerable del cuerpo e imagen de la mujer, bajo el pretexto del erotismo, la sexualidad o el mito. El surrealismo abrió sus puertas a más creadoras que ningún otro movimiento de vanguardia, sin embargo, su insistencia en el entendimiento de las mismas como sujetos poéticos, hizo que desaprovechase la oportunidad de revisar, objetivamente, las motivaciones femeninas. Basta observar las creaciones de muchas artistas surrealistas para apreciar que sus intereses no tienen nada que ver con los de sus colegas masculinos: pocas veces se identificaron con la mitología erótica o la simbología freudiana, construyendo un imaginario que ponía en valor sus propias circunstancias personales. Como punto de partida metodológico, nos hemos servido de una publicación relativamente reciente (2005) de Alyce Mahon, Surrealismo, Eros y política (1938-1968), la cual reivindica la vigencia del surrealismo valiéndose de argumentos filosóficos, idealismos y mitos. Contrastando los puntos de vista de Mahon con las contribuciones de figuras clave del feminismo y estudiosas del surrealismo, aportamos una serie de reflexiones sobre las estructuras ocultas de poder, tratando de desenmascarar aquellos relatos que, tras una supuesta objetividad, manifiestan un continuismo ideológico con los mecanismos de poder tradicionales. Asimismo, planteamos la necesidad de permanecer vigilantes sobre las fuentes divulgativas y las metodologías docentes y académicas, en aras de contextualizar y analizar críticamente los excesos de determinadas ideologías artísticas.

Madeleine Woodburn, Wynita M. Griggs, Jakub Marecek et al.

Over two thirds of women of all ages in the UK have experienced some form of sexual harassment in a public space. Recent tragic incidents involving female pedestrians have highlighted some of the personal safety issues that women still face in cities today. There exist many popular location-based safety applications as a result of this; however, these applications tend to take a reactive approach where action is taken only after an incident has occurred. This paper proposes a preventative approach to the problem by creating safer public environments through societal incentivisation. The proposed system, called "Herd Routes", improves the safety of female pedestrians by generating busier pedestrian routes as a result of route incentivisation. A novel application of distributed ledgers is proposed to provide security and trust, a record of system users' locations and IDs, and a platform for token exchange. A proof-of-concept was developed using the simulation package SUMO (Simulation of Urban Mobility), and a smartphone app. was built in Android Studio so that pedestrian Hardware-in-the-Loop testing could be carried out to validate the technical feasibility and desirability of the system. With positive results from the initial testing of the proof-of-concept, further development could significantly contribute towards creating safer pedestrian routes through cities, and tackle the societal change that is required to improve female pedestrian safety in the long term.

Judith K. Morgan, Judith K. Morgan, Hendrik Santosa et al.

Although there has been growing interest in mood-related neural alterations in women in the initial weeks postpartum, recent work has demonstrated that postpartum depression often lingers for months or years following birth. However, research evaluating the impact of depression on maternal brain function during mother-infant interactions in the late postpartum period is lacking. The current study tested the hypothesis that depressive symptoms at 12-months postpartum are associated with neural alterations in affective and social neural regions, using near-infrared spectroscopy during in vivo mother-infant interactions. Participants were 23 birth mothers of 12-month-old infants (60% boys). While undergoing near-infrared spectroscopy, mothers engaged in an ecologically valid interactive task in which they looked at an age-appropriate book with their infants. Mothers also reported on their depressive symptoms in the past week and were rated on their observed levels of maternal sensitivity during mother-infant play. Greater depressive severity at 12-months postpartum was related to lower connectivity between the right temporoparietal junction and the lateral prefrontal cortex, but greater connectivity between the right temporoparietal junction and anterior medial prefrontal cortex during mother-infant interaction. Given the putative functions of these neural regions within the maternal brain network, our findings suggest that in the context of depression, postpartum mothers' mentalizing about her infants' thoughts and feelings may be related to lower ability to express and regulate her own emotions, but greater ability to engage in emotional bonding with her infant. Future work should explore how connectivity among these regions is associated with longitudinal changes in maternal behavior, especially in the context of changes in mothers' depressive symptoms (e.g., with treatment) over time.

Sukesi Rahayu, Katrhryn Emerson, Phakkharawat Sittiprapaporn

Abstract Feminism in Sukesi Rahayu's Jineman Kenya Ndesa laras slendro pathet sanga is a study that reviews feminist discourses on the creation of gamelan music based on the issues of gender equality between women and men. The purpose of this research is to prove and show that the creation of Javanese karawitan is not only based on male paradigm domination, but women also have a role in speaking out about feminism through karawitan works. The research methodology used is descriptive qualitative by positioning the object of study as the primary focus and writings on feminism as supporting sources. The results of this study indicate that in Sukesi Rahayu's Jineman Kenya Ndesa Slendro Sanga, there is feminist content, namely an attempt to elevate the dignity of women, which in this case is sindhen, within the scope of Javanese art culture as well as women in general. Keywords: Feminism; Sindhenan; Javanese culture

Mariana Macedo, Laura Lotero, Alessio Cardillo et al.

Many of our routines and activities are linked to our ability to move; be it commuting to work, shopping for groceries, or meeting friends. Yet, factors that limit the individuals' ability to fully realise their mobility needs will ultimately affect the opportunities they can have access to (e.g., cultural activities, professional interactions). One important aspect frequently overlooked in human mobility studies is how gender-centred issues can amplify other sources of mobility disadvantages (e.g., socioeconomic inequalities), unevenly affecting the pool of opportunities men and women have access to. In this work, we leverage on a combination of computational, statistical, and information-theoretical approaches to investigate the existence of systematic discrepancies in the mobility diversity (i.e., the diversity of travel destinations) of (1) men and women from different socioeconomic backgrounds, and (2) work and non-work travels. Our analysis is based on datasets containing multiple instances of large-scale, official, travel surveys carried out in three major metropolitan areas in South America: Medellín and Bogotá in Colombia, and São Paulo in Brazil. Our results indicate the presence of general discrepancies in the urban mobility diversities related to the gender and socioeconomic characteristics of the individuals. Lastly, this paper sheds new light on the possible origins of gender-level human mobility inequalities, contributing to the general understanding of disaggregated patterns in human mobility.

Hala Mulki, Bilal Ghanem

Online misogyny has become an increasing worry for Arab women who experience gender-based online abuse on a daily basis. Misogyny automatic detection systems can assist in the prohibition of anti-women Arabic toxic content. Developing such systems is hindered by the lack of the Arabic misogyny benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset for Arabic misogyny. We further provide a detailed review of the dataset creation and annotation phases. The consistency of the annotations for the proposed dataset was emphasized through inter-rater agreement evaluation measures. Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration. The obtained results indicated that the performances achieved by the used systems are consistent with state-of-the-art results for languages other than Arabic, while employing MTL improved the performance of the misogyny/target classification tasks.

Geovana Quinalha Oliveira

BITTELBRUN, Gabrielle. Cores e contornos: Gênero e raça em revistas femininas do século 21. Florianópolis: Editora Insular, 2018, 388p.

Procheta Sen, Debasis Ganguly

Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice. The prevalent existence of cognitive biases in large volumes of historical data can pose a threat of being manifested as unethical and seemingly inhuman predictions as outputs of AI systems trained on such data. To alleviate this problem, we propose a bias-aware multi-objective learning framework that given a set of identity attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories of the possible classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as `most blacks use abusive language', or `fear is a virtue of women'. Our experiments conducted on an emotion prediction task with balanced class priors shows that a set of baseline bias-agnostic models exhibit cognitive biases with respect to gender, such as women are prone to be afraid whereas men are more prone to be angry. In contrast, our proposed bias-aware multi-objective learning methodology is shown to reduce such biases in the predictied emotions.