Hasil untuk "Women. Feminism"

Aalya Ahmad

Sarah Ahmed’s Living a Feminist Life is much more than a farewell to her institutional academic life in the wake of her highly publicized resignation from Goldsmiths, the University of London, in protest of the university’s handling of sexual harassment. Her latest work retains a fierce grip on the spirit of feminist critical theory, while avowing that it is possible and even powerful to ‘leave a life’ that is not feminist. It is customary to begin studying feminism by defining it as a ‘life question’. For educators, such discussions flow into rewarding ‘clicks’ of transformative political consciousness when students re-examine their own experiences in the light of feminist theory. Ahmed, writing explicitly for students, begins by considering what it means ‘to make everything into something that is questionable’ (p. 2), recounting her own ‘clicking’ moments: ‘I began to realize what I already knew: that patriarchal reasoning goes all the way down, to the letter, to the bone’ (p. 4). In laying out a foundational self-reflexivity, constantly connecting her background in philosophy (the letter) with her life (bone) as a brown lesbian feminist of mixed heritage, Ahmed shows us how to re-politicize the personal: ‘I began to appreciate that theory can do more the closer it gets to the skin’ (p. 10). In urging feminists, ‘do not become the master’s tool!’ (p. 160), Ahmed invokes Audre Lorde’s well-known concept of the master’s house: ‘I had to find ways not to reproduce its grammar in what I said, in what I wrote, in what I did, in who I was’ (p. 4). Her own refusal to be a master’s tool crops up in, for example, Ahmed’s policy of citing feminists of colour rather than white men, because citations ‘are the materials through which, from which, we create our dwellings’ (p. 16). Feminism’s fault lines (such as excluding trans women) show us the cracks in dogmatic certainty: Ahmed argues that a ‘feminist tendency ... does not give us a stable ground’ (p. 7). Stability mires feminisms in injustice. Instead we must learn to reject what Alexis Shotwell (2016) describes as a politics of ‘purity’, and instead embrace the idea that feminism’s houses may be reconstructed and deconstructed on shifting terrain.

K. Crenshaw

D. Haraway

Chandra Talpade Mohanty

Gayle S. Rubin

A. Brah

A. Rich

Judith Grant

Gentian Vyshka, Ermir Roçi, Entela Basha

Myriam Patricia Jara Peña

La educación intercultural en Latinoamérica y en particular en Chile, necesita ser revisada y actualizada, para mejorar la convivencia entre las y los ciudadanos (Arredondo & Paidacán, 2023). Este artículo presenta una revisión descriptiva de la literatura sobre investigaciones, que se encuentran plasmadas en tres textos de las autoras: Pequeño (2009); Rousseau y Morales (2018) y Ulloa (2020). Son compilaciones de trabajos sobre mujeres indígenas en política y movimientos de mujeres indígenas en Latinoamérica, sus roles, vivencias, formas de organización y luchas. Si bien, entre los fundamentos de la educación intercultural es la comprensión del otro. Las teorías de la comunicación intercultural evidencian que las fallidas políticas públicas han surgido por desconocimiento a ese otro, (Del Valle, 2009; Grimson, 2001 y  Rodrigo, 1999). El objetivo es, por lo tanto, describir la literatura para conocer y comprender las formas de organización de las mujeres indígenas en política y aportar en la educación intercultural. Desde la perspectiva de género con el conocimiento situado, (Haraway, 1991 y Bidaseca, 2022) se crean los criterios de inclusión (género, contexto y temática). En consecuencia, las tres autoras de esta investigación establecen que la invisibilización que han sufrido las mujeres indígenas ha dado pie al desconocimiento de las razones de fondo de sus luchas. Se hace necesario comprender que la comunidad unida, educada y organizada logra mantener su identidad.

Breno Mota Alvarenga

The concept of queer temporality is explored in queer studies as a possibility for engaging with time in other ways than the straight time framework, which is anchored in a heteronormativity idea of personal progress, encompassing conjugal matrimony, reproduction and monetary inheritance. Inspired by Carla Freccero’s perspective of queer spectrality (2006), this article aims to investigate how some autobiographical films invite the spectator to glimpse into ways of experiencing queer temporalities in audiovisual narratives, where past, present and future may coincide. Specifically, I analyze the use of audiovisual disjunction as a cinematic technique that manifests a sense of asynchronicity and being “out of joint.” In this regard, I do a parallel reading of two autobiographical films directed by gay filmmakers: Blue (1993), by Derek Jarman, and The Terence Davies Trilogy (1983), by Terence Davies. Hence, this article aims to elucidate the use of soundtracks in these films as kinesthetic devices, enabling the audience to momentarily perceive queer temporalities through auditory experiences.

Gustavo A. Basílio, Thiago B. Pereira, Alessandro L. Koerich et al.

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early screening is crucial for effective intervention, as timely identification of mental health issues can significantly improve treatment outcomes. Artificial intelligence (AI) can be valuable for improving the screening of mental disorders, enabling early intervention and better treatment outcomes. AI-driven screening can leverage the analysis of multiple data sources, including facial features in digital images. However, existing methods often rely on controlled environments or specialized equipment, limiting their broad applicability. This study explores the potential of AI models for ubiquitous depression-anxiety screening given face-centric selfies. The investigation focuses on high-risk pregnant patients, a population that is particularly vulnerable to mental health issues. To cope with limited training data resulting from our clinical setup, pre-trained models were utilized in two different approaches: fine-tuning convolutional neural networks (CNNs) originally designed for facial expression recognition and employing vision-language models (VLMs) for zero-shot analysis of facial expressions. Experimental results indicate that the proposed VLM-based method significantly outperforms CNNs, achieving an accuracy of 77.6%. Although there is significant room for improvement, the results suggest that VLMs can be a promising approach for mental health screening.

Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh et al.

Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding the factors that drive contraceptives selection and switching is of significant interest. However, many factors related to medication switching are often only captured in unstructured clinical notes and can be difficult to extract. Here, we evaluate the zero-shot abilities of a recently developed large language model, GPT-4 (via HIPAA-compliant Microsoft Azure API), to identify reasons for switching between classes of contraceptives from the UCSF Information Commons clinical notes dataset. We demonstrate that GPT-4 can accurately extract reasons for contraceptive switching, outperforming baseline BERT-based models with microF1 scores of 0.849 and 0.881 for contraceptive start and stop extraction, respectively. Human evaluation of GPT-4-extracted reasons for switching showed 91.4% accuracy, with minimal hallucinations. Using extracted reasons, we identified patient preference, adverse events, and insurance as key reasons for switching using unsupervised topic modeling approaches. Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations. Our code and supplemental data are available at https://github.com/BMiao10/contraceptive-switching.

Abbey E. Corson, Meaghan MacDonald, Velislava Tzaneva et al.

Historically, females were excluded from clinical research due to their reproductive roles, hindering medical understanding and healthcare quality. Despite guidelines promoting equal participation, females are underrepresented in exercise science, perpetuating misconceptions about female physiology. Even less attention has been given to exercise in the pregnant population. Research on pregnancy and exercise has evolved considerably from the initial bedrest prescriptions but concerns about exercise risks during pregnancy persisted for many decades. Recent guidelines endorse moderate-intensity physical activity during pregnancy, supported by considerable evidence of its safety and benefits. Mental health during pregnancy, often overlooked, is gaining traction, with exercise showing promise in reducing depression and anxiety. While pregnancy guidelines recommend moderate-intensity physical activity, there remains limited understanding of optimal frequency, intensity, type and time (duration) for extremes like elite athletes or those with complications. Female participation in elite sport and physically demanding jobs is rising, but research on their specific needs is lacking. Traditional practices like bed rest for high-risk pregnancies are being questioned, as evidence suggests it may not improve outcomes. Historical neglect of gestational parents in research perpetuated stereotypes of female frailty, but recent years have seen a shift towards recognizing the benefits of an active pregnancy. Closing knowledge gaps and inclusivity in research are crucial for ensuring guidelines reflect the diverse needs of gestational parents. Therefore, the purpose of this review is to summarize the evolution of exercise physiology and pregnancy research along with future directions for this novel field.

Angella Ndaka, Harriet Ratemo, Abigail Oppong et al.

The study examines how ontonorms propagate certain gender practices in digital spaces through character and the norms of spaces that shape AI design, training and use. Additionally the study explores the different user behaviours and practices regarding whether, how, when, and why different gender groups engage in and with AI driven spaces. By examining how data and content can knowingly or unknowingly be used to drive certain social norms in the AI ecosystems, this study argues that ontonorms shape how AI engages with the content that relates to women. Ontonorms specifically shape the image, behaviour, and other media, including how gender identities and perspectives are intentionally or otherwise, included, missed, or misrepresented in building and training AI systems.

Dr. M. Vinoth Kumar

Elizabeth de Burgh is depicted as a nurturing and supportive mother to her children, particularly to her husband Robert Bruce. She possesses a maternal instinct that provides emotional support, guidance, and strength to her children and husband through difficult times as the Great Mother Archetype. As a Queen Archetype, she is highlighted by her regal dignity, wisdom, and leadership abilities. Elizabeth’s marriage to Robert Bruce and her interactions with the English Court all illustrate her representation of this archetype. Moreover, Elizabeth represents the Freedom Archetype through her unwavering commitment to Scotland’s independence. Her active participation in her husband ’s plan for Scottish independence displays her passion for freedom and her willingness to fight for it. Elizabeth de Burgh embodies three archetypes in Nigel Tranter’s “The Bruce Trilogy”, the Great Mother Archetype, Queen Archetype, and Freedom Archetype. Her character development and actions throughout the plot demonstrate the importance of these archetypes. Her representation of these archetypes shows her as a sanguine, intelligent, talented, safeguarding, complex and multifaceted character, with the ability to be both compassionate and formidable, nurturing and commanding, and loyal to both her family and country. Her character serves as an inspiration for women to embody multiple archetypes and to remain steadfast in their values and beliefs. Hence, this paper traces such evidence of archetypal elements in the trilogy.

Kshitiz Verma

Indian Judiciary is suffering from burden of millions of cases that are lying pending in its courts at all the levels. In this paper, we analyze the data that we have collected on the pendency of 24 high courts in the Republic of India as they were made available on High Court NJDG (HC-NJDG). We collected data on 73 days beginning August 31, 2017 to December 26, 2018, including these days. Thus, the data collected by us spans a period of almost sixteen months. We have analyzed various statistics available on the NJDG portal for High Courts, including but not limited to the number of judges in each high court, the number of cases pending in each high court, cases that have been pending for more than 10 years, cases filed, listed and disposed, cases filed by women and senior citizens, etc. Our results show that: 1) statistics as important as the number of judges in high courts have serious errors on NJDG (Fig. 1, 2, 10, 11, Table V). 2) pending cases in most of the high courts are increasing rather than decreasing (Fig. 3, 13). 3) regular update of HC-NJDG is required for it to be useful. Data related to some high courts is not being updated regularly or is updated erroneously on the portal (Fig. 14). 4) there is a huge difference in terms of average load of cases on judges of different high courts (Fig. 6). 5) if all the high courts operate at their approved strength of judges, then for most of the high courts pendency can be nullified within 20 years from now (Fig. 21, 22). 6) the pending cases filed by women and senior citizens are disproportionately low, they together constitute less than 10% of the total pending cases (Fig. 23 - 27) 7) a better scheduling process for preparing causelists in courts can help reducing the number of pending cases in the High Courts (Fig. 29). 8) some statistics are not well defined (Fig. 31).

Trevor Tsue, Brent Mombourquette, Ahmed Taha et al.

This work reveals undiscovered challenges in the performance and generalizability of deep learning models. We (1) identify spurious shortcuts and evaluation issues that can inflate performance and (2) propose training and analysis methods to address them. We trained an AI model to classify cancer on a retrospective dataset of 120,112 US exams (3,467 cancers) acquired from 2008 to 2017 and 16,693 UK exams (5,655 cancers) acquired from 2011 to 2015. We evaluated on a screening mammography test set of 11,593 US exams (102 cancers; 7,594 women; age 57.1 \pm 11.0) and 1,880 UK exams (590 cancers; 1,745 women; age 63.3 \pm 7.2). A model trained on images of only view markers (no breast) achieved a 0.691 AUC. The original model trained on both datasets achieved a 0.945 AUC on the combined US+UK dataset but paradoxically only 0.838 and 0.892 on the US and UK datasets, respectively. Sampling cancers equally from both datasets during training mitigated this shortcut. A similar AUC paradox (0.903) occurred when evaluating diagnostic exams vs screening exams (0.862 vs 0.861, respectively). Removing diagnostic exams during training alleviated this bias. Finally, the model did not exhibit the AUC paradox over scanner models but still exhibited a bias toward Selenia Dimension (SD) over Hologic Selenia (HS) exams. Analysis showed that this AUC paradox occurred when a dataset attribute had values with a higher cancer prevalence (dataset bias) and the model consequently assigned a higher probability to these attribute values (model bias). Stratification and balancing cancer prevalence can mitigate shortcuts during evaluation. Dataset and model bias can introduce shortcuts and the AUC paradox, potentially pervasive issues within the healthcare AI space. Our methods can verify and mitigate shortcuts while providing a clear understanding of performance.

Jussi Heikkilä, Ina Laukkanen

We document that existing gender equality indices do not account for gender-specific mandatory peace-time conscription (compulsory military service). This suggests that gender-specific conscription is not considered to be an important gender issue. If an indicator measuring the gender equality of mandatory conscription was to be included in gender equality indices with appropriate weight, then the relative rankings of countries in terms of measured gender equality could be affected. In the context of the Nordic countries, this would mean that Finland and Denmark - the countries with mandatory conscription for men only - would have worse scores with respect to gender equality compared to Sweden and Norway, countries with conscription for both men and women - and Iceland, which has no mandatory conscription, regardless of gender.

Elsayed Ghanem, Armin Hatefi, Hamid Usefi

In many applications (e.g., medical studies), the population of interest (e.g., disease status) comprises heterogeneous subpopulations. The mixture of probabilistic regression models is one of the most common techniques to incorporate the information of covariates into learning of the population heterogeneity. Despite its flexibility, the model may lead to unreliable estimates in the presence of multicollinearity problem. In this paper, we develop Liu-type shrinkage methods through an unsupervised learning approach to estimate the model coefficients in multicollinearity. The performance of the developed methods is evaluated via classification and stochastic versions of EM algorithms. The numerical studies show that the proposed methods outperform their Ridge and maximum likelihood counterparts. Finally, the developed methods are applied to analyze the bone mineral data of women aged 50 and older.