Hasil untuk "Women. Feminism"

J. Benjamin

S. Okin

L. Alcoff

Maile Arvin, E. Tuck, Angie Morrill

The article explores two intertwined ideas: that the United States is a settler colonial nation-state and that settler colonialism has been and continues to be a gendered process. The article engages Native feminist theories to excavate the deep connections between settler colonialism and heteropatriarchy, highlighting five central challenges that Native feminist theories pose to gender and women's studies. From problematizing settler colonialism and its intersections to questioning academic participation in Indigenous dispossession, responding to these challenges requires a significant departure from how gender and women's studies is regularly understood and taught. Too often, the consideration of Indigenous peoples remains rooted in understanding colonialism as an historical point in time away from which our society has progressed. Centering settler colonialism within gender and women's studies instead exposes the still-existing structure of settler colonialism and its powerful effects on Indigenous peoples and settlers. Taking as its audience practitioners of both "whitestream" and other feminisms and writing in conversation with a long history of Native feminist theorizing, the article offers critical suggestions for the meaningful engagement of Native feminisms. Overall, it aims to persuade readers that attending to the links between heteropatriarchy and settler colonialism is intellectually and politically imperative for all peoples living within settler colonial contexts.

Vasudha Malhotra, Rhea D'silva, Rashina Hoda

Despite their critical role in shaping student learning in computing education, the contributions of women teaching-support staff (TSS) often go unrecognised and undervalued. In this experience report, we synthesise lived experiences of 15 women TSS in IT/SE higher education to illuminate how authority is earned, resisted, and maintained in everyday teaching. Participants shared both their positive and negative lived experiences associated with finding and losing voice with teaching team colleagues on the one hand, and rewarding connections and gendered friction with students on the other. We map these dynamics onto an intersectional "wheel of privilege and power" tailored to TSS roles. The farther a TSS profile sits from the wheel's center (e.g., non-native English, non-white, younger-seeming, non-permanent, early-career), the more relational, emotional, and disciplinary labour is needed to reach parity. We provide actionable insights and recommendations for creating more inclusive education environments in technology dominant fields that are particularly timely as universities worldwide grapple with post-pandemic teaching models and seek to build more inclusive and resilient academic communities.

Mona A. Mohamed, Melissa Banks, Sina Khazaee Nejad et al.

Abstract Acetaminophen is the most common cause of acute liver failure in the United States. The transfer of drugs to breast milk poses risks to infants, yet dosing guidelines are based on limited data from small studies. Currently, drug levels in milk are simply estimated through invasive blood draws. We present a new low-cost, textile-based electrochemical sensor for detecting acetaminophen at the point of use in breast milk. An embroidered conductive yarn (steel and silver) is used, which eliminates the need for complex microfabrication processes. A gold-nanoparticle-doped carbon ink-modified steel yarn serves as the working electrode, with pristine steel and silver yarns as counter and reference electrodes, respectively. Using square wave voltammetry, the sensor achieves a linear detection range of 9.9–166.4 μM in undiluted breast milk, with a limit of detection of 1.15 μM. This platform provides a simple and accessible alternative for drug monitoring

Marc Gandarillas

Both empirical and anecdotal evidence suggests that there is an intricate relationship between gender and language. This can be analyzed from a number of different―yet complementary―perspectives. As early as the 1980s, research articles can be found that explore the way in which gender, power, and dominance interact in mixed-sex talk (West & Zimmerman, 1983; DeFrancisco, 1991; Herring et al., 1992). Complementarily, the complex relationship between gender and language has been approached from a same-sex talk perspective, both in female-only (Coates, 1989) and male-only settings (DeCapua & Boxer, 1999; Cameron, 1997). Beyond the private domain, women’s talk has been studied within the specific―and of utmost interest―framework provided by the public sphere (Reynolds, 1991; Holmes & Schnurr, 2006). In more recent times, sexuality has been added to the combination in an attempt to obtain a bigger―and clearer―picture (Abe, 2004; Hall, 2009; Leap, 2008). Some scholars have even traveled the extra mile by providing insightful approaches to the relationship between gender, language, and identity by establishing explicit connections between these concepts and related cultural practices (relevantly to this topic, Boxer & Gritsenko (2005) compare how women in the US and Russia tackle the surname issue when faced with marriage or partnership).

. .

Palash Ray, Mahuya Sasmal, Asish Bera

Sports action classification representing complex body postures and player-object interactions is an emerging area in image-based sports analysis. Some works have contributed to automated sports action recognition using machine learning techniques over the past decades. However, sufficient image datasets representing women sports actions with enough intra- and inter-class variations are not available to the researchers. To overcome this limitation, this work presents a new dataset named WomenSports for women sports classification using small-scale training data. This dataset includes a variety of sports activities, covering wide variations in movements, environments, and interactions among players. In addition, this study proposes a convolutional neural network (CNN) for deep feature extraction. A channel attention scheme upon local contextual regions is applied to refine and enhance feature representation. The experiments are carried out on three different sports datasets and one dance dataset for generalizing the proposed algorithm, and the performances on these datasets are noteworthy. The deep learning method achieves 89.15% top-1 classification accuracy using ResNet-50 on the proposed WomenSports dataset, which is publicly available for research at Mendeley Data.

Maximilian Rokuss, Benjamin Hamm, Yannick Kirchhoff et al.

We introduce the first publicly available breast MRI dataset with explicit left and right breast segmentation labels, encompassing more than 13,000 annotated cases. Alongside this dataset, we provide a robust deep-learning model trained for left-right breast segmentation. This work addresses a critical gap in breast MRI analysis and offers a valuable resource for the development of advanced tools in women's health. The dataset and trained model are publicly available at: www.github.com/MIC-DKFZ/BreastDivider

Shalini Saini, Nitesh Saxena

FemTech, a rising trend in mobile apps, empowers women to digitally manage their health and family planning. However, privacy and security vulnerabilities in period-tracking and fertility-monitoring apps present significant risks, such as unintended pregnancies and legal consequences. Our approach involves manual observations of privacy policies and app permissions, along with dynamic and static analysis using multiple evaluation frameworks. Our research reveals that many of these apps gather personally identifiable information (PII) and sensitive healthcare data. Furthermore, our analysis identifies that 61% of the code vulnerabilities found in the apps are classified under the top-ten Open Web Application Security Project (OWASP) vulnerabilities. Our research emphasizes the significance of tackling the privacy and security vulnerabilities present in period-tracking and fertility-monitoring mobile apps. By highlighting these crucial risks, we aim to initiate a vital discussion and advocate for increased accountability and transparency of digital tools for women's health. We encourage the industry to prioritize user privacy and security, ultimately promoting a safer and more secure environment for women's health management.

Sonja Cwik, Chandralekha Singh

Student beliefs in introductory physics courses can influence their course outcomes and retention in STEM disciplines and future career aspirations. This study used survey data from 501 students in the first of two-semester algebra-based introductory physics courses primarily taken by bioscience majors, in which women make up approximately 65% of the class. We investigated how the learning environment including perceived recognition, peer interaction, and sense of belonging correlate with students' physics outcomes, including their physics self-efficacy, interest, and identity. We found that in general, women had lower physics beliefs than men and the learning environment plays a major role in explaining student outcomes. We also found that perceived recognition played an important role in predicting students' physics identity and students' sense of belonging played an important role in predicting students' physics self-efficacy in the first algebra-based introductory physics course investigated. These findings can be useful to contemplate strategies to create an equitable and inclusive learning environment to help all students to excel in these physics courses.

Heloisa Candello, Gabriel Meneguelli Soella, Leandro de Carvalho Nascimento

Small business owners (SBOs), specially women, face several challenges in everyday life, especially when asking for microcredit loans from financial institutions. Usual difficulties include low credit scores, unbaked situations, outstanding debts, informal employment situations, inability to showcase their payable capacity, and lack of financial guarantor. Moreover, SBOs often need help applying for microcredit loans due to the lack of information on how to proceed. The task of asking for a loan is a complex practice, and asymmetric power relationships might emerge, but that benefits micro-entrepreneurs only sometimes. In this paper, we interviewed 20 women entrepreneurs living in a low-income community in Brazil. We wanted to unveil value tensions derived from this practice that might influence the design of AI technologies for the public. In doing so, we used a conversational system as a probe to understand the opportunities for empowering their practices with the support of AI multimedia conversational systems. We derived seven recommendations for designing AI systems for evaluating micro-business health in low-income communities.

Madhumita Pandey

Research involving prisoners is a vital source of information on crime but is often fraught with several challenges. This article presents an analysis of one of the first prison researches conducted in India with men convicted of rape. It examines and expands on the nuances of interacting with men convicted of rape and exploring a range of deeply personal questions with them. The research analysis attempts to highlight the impact of the researcher's positionality on offender accounts by also discussing social proximity and gender. This article contributes to the broader discourse around conducting qualitative research in prisons.

Begüm TOPRAK

Julieta Berriel

En los discursos políticos de las mujeres mapuce se configuran representaciones colectivas y una autorrepresentación signada por el género y la racialización. Desde el Análisis Crítico del Discurso (ACD), en este trabajo se analizan los discursos orales emitidos por activistas mapuce en el 33º Encuentro Nacional de Mujeres, realizado en 2018 en la ciudad de Trelew (Chubut, Argentina). El objetivo que se persigue es evidenciar las estrategias de legitimación de autorrepresentaciones y los medios de autolegitimación subjetiva, fundamentales para explicar las representaciones de las mujeres mapuce en el Movimiento Amplio de Mujeres. 

Rebecca Godderis

Luhang Sun, Mian Wei, Yibing Sun et al.

Generative AI models like DALL-E 2 can interpret textual prompts and generate high-quality images exhibiting human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL-E 2 images spanning 153 occupations, and assessed potential bias amplification by benchmarking against 2021 census labor statistics and Google Images. Our findings reveal that DALL-E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL-E 2 images tend to depict more women than men with smiling faces and downward-pitching heads, particularly in female-dominated (vs. male-dominated) occupations. Our computational algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL-E 2 compared to Google Images and calls for feminist interventions to prevent such bias-laden AI-generated images to feedback into the media ecology.

Elisabeth Prügl

Natalia Casola

Este artículo reconstruye la política del Partido Obrero (PO) hacia el movimiento de mujeres y las militancias femeninas, en el largo periodo que va desde su fundación en 1964 hasta la conformación en 1998 de la agrupación Plenario Autoconvocado de Trabajadoras. El objetivo es analizar cómo el PO integró el problema de la opresión femenina a su estrategia: ¿cuándo, y de qué manera ingresó la problemática dentro del partido y qué líneas específicas desarrollaron para vincularse con el movimiento de mujeres y con el feminismo en particular? La hipótesis del trabajo es que el ascenso del feminismo y la organización regular de los Encuentros Nacionales de Mujeres permitieron dislocar sentidos ya establecidos y establecer otros que ayudaron al partido a definir una línea propia.