Hasil untuk "Women. Feminism"

Nils Lid Hjort

Suppose some cleverness score parameter is sufficiently interesting to be defined and then measured, perhaps for different strata of specialists or for the broader population. Such phenomena could have Gaussian distributions, when it comes to all players in a stratum, but when interest focuses on the very tails, for the top few percent, those above certain high thresholds, different models are called for, along with the need to analyse such based on the listed top scores only. In this note I develop such models and tools, and apply them to the top-100 and above 2100 points lists for regular chess ratings, for the currently active 14671 men and 753 women, as given by the FIDE, January 2026. It is argued that even when two or more distributions have close to identical expected values, or medians, even smaller differences in variance may explain gaps for the few very best ones.

Ina Kaleva, Xiao Zhan, Ruba Abu-Salma et al.

The rapid adoption of generative AI (GenAI) chatbots has reshaped access to sexual and reproductive health (SRH) information, particularly following the overturning of Roe v. Wade, as individuals assigned female at birth increasingly turn to online sources. However, existing research remains largely model-centered, paying limited attention to user privacy and safety. We conducted semi-structured interviews with 18 U.S.-based participants from both restrictive and non-restrictive states who had used GenAI chatbots to seek SRH information. Adoption was influenced by perceived utility, usability, credibility, accessibility, and anthropomorphism, and many participants disclosed sensitive personal SRH details. Participants identified multiple privacy risks, including excessive data collection, government surveillance, profiling, model training, and data commodification. While most participants accepted these risks in exchange for perceived utility, abortion-related queries elicited heightened safety concerns. Few participants employed protective strategies beyond minimizing disclosures or deleting data. Based on these findings, we offer design and policy recommendations, such as health-specific features and stronger moderation practices, to enhance privacy and safety in GenAI-supported SRH information seeking.

Emily Winskill

This paper investigates the relationship between job mobility and earnings growth in the UK labour market, with a focus on gender differences in the returns to switching jobs. Using data from the Annual Survey of Hours and Earnings (ASHE) between 2011 and 2023, the analysis compares wage progression for job switchers and stayers, controlling for individual and job characteristics. The findings show that job mobility is associated with higher earnings growth, but women experience smaller gains than men, with occupational mobility and age further widening this gap. However, the study finds no statistically significant evidence that changes in occupation, sector, or working time pattern influence this gender gap. The results highlight the importance of addressing gender disparities in the returns to job mobility and provide valuable evidence for developing policy interventions aimed at promoting more equitable labour market outcomes.

Hambur Wang

The effectiveness of farmer loan policies is crucial for the high-quality development of agriculture and the orderly advancement of the rural revitalization strategy. Exploring the impact of farmers' borrowing behavior on agricultural production technical efficiency holds significant practical value. This paper utilizes data from the 2020 China Family Panel Studies (CFPS) and applies Stochastic Frontier Analysis (SFA) along with the Tobit model for empirical analysis. The study finds that farmers' borrowing behavior positively influences agricultural production technical efficiency, with this effect being especially pronounced among low-income farmers. Additionally, the paper further examines household characteristics, such as household head age, gender, educational level, and the proportion of women in the family, in relation to agricultural production technical efficiency. The findings provide policy recommendations for optimizing rural financial service systems and enhancing agricultural production technical efficiency.

Yixin Wan, Kai-Wei Chang

Social biases can manifest in language agency. However, very limited research has investigated such biases in Large Language Model (LLM)-generated content. In addition, previous works often rely on string-matching techniques to identify agentic and communal words within texts, falling short of accurately classifying language agency. We introduce the Language Agency Bias Evaluation (LABE) benchmark, which comprehensively evaluates biases in LLMs by analyzing agency levels attributed to different demographic groups in model generations. LABE tests for gender, racial, and intersectional language agency biases in LLMs on 3 text generation tasks: biographies, professor reviews, and reference letters. Using LABE, we unveil language agency social biases in 3 recent LLMs: ChatGPT, Llama3, and Mistral. We observe that: (1) LLM generations tend to demonstrate greater gender bias than human-written texts; (2) Models demonstrate remarkably higher levels of intersectional bias than the other bias aspects. (3) Prompt-based mitigation is unstable and frequently leads to bias exacerbation. Based on our observations, we propose Mitigation via Selective Rewrite (MSR), a novel bias mitigation strategy that leverages an agency classifier to identify and selectively revise parts of generated texts that demonstrate communal traits. Empirical results prove MSR to be more effective and reliable than prompt-based mitigation method, showing a promising research direction.

Tobias Rohrbach, Mykola Makhortykh, Maryna Sydorova

Search engines like Google have become major information gatekeepers that use artificial intelligence (AI) to determine who and what voters find when searching for political information. This article proposes and tests a framework of algorithmic representation of minoritized groups in a series of four studies. First, two algorithm audits of political image searches delineate how search engines reflect and uphold structural inequalities by under- and misrepresenting women and non-white politicians. Second, two online experiments show that these biases in algorithmic representation in turn distort perceptions of the political reality and actively reinforce a white and masculinized view of politics. Together, the results have substantive implications for the scientific understanding of how AI technology amplifies biases in political perceptions and decision-making. The article contributes to ongoing public debates and cross-disciplinary research on algorithmic fairness and injustice.

Abdulqader Mohammed, Mohammed Abdel Razek, Mohamed El-dosuky et al.

Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. This work proposed a deep learning system that increased the accuracy of classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, the accuracy of proposed system was evaluated.

Elsayed Ghanem, Armin Hatefi, Hamid Usefi

The logistic regression model is one of the most powerful statistical methods for the analysis of binary data. The logistic regression allows to use a set of covariates to explain the binary responses. The mixture of logistic regression models is used to fit heterogeneous populations through an unsupervised learning approach. The multicollinearity problem is one of the most common problems in logistics and a mixture of logistic regressions where the covariates are highly correlated. This problem results in unreliable maximum likelihood estimates for the regression coefficients. This research developed shrinkage methods to deal with the multicollinearity in a mixture of logistic regression models. These shrinkage methods include ridge and Liu-type estimators. Through extensive numerical studies, we show that the developed methods provide more reliable results in estimating the coefficients of the mixture. Finally, we applied the shrinkage methods to analyze the bone disorder status of women aged 50 and older.

Roel F. Ceballos

This study aims to determine the demographic, epidemiologic, and clinical characteristics of COVID-19 cases that are highly susceptible to COVID-19 infection, with longer hospitalization and at higher risk of mortality and to provide insights that may be useful to assess the vaccination priority program and allocate hospital resources. Methods that were used include descriptive statistics, nonparametric analysis, and survival analysis. Results of the study reveal that women are more susceptible to infection while men are at risk of longer hospitalization and higher mortality. Significant risk factors to COVID-19 mortality are older age, male sex, difficulty breathing, and comorbidities like hypertension and diabetes. Patients with these combined symptoms should be considered for admission to the COVID-19 facility for proper management and care. Also, there is a significant delay in the testing and diagnosis of those who died, implying that timeliness in the testing and diagnosis of patients is crucial in patient survival.

Sema Gunturkun

The Eisenbud-Green-Harris (EGH) conjecture offers a generalization of the famous Macaulay's theorem about the Hilbert functions of homogeneous ideals in a polynomial ring $K[x_1,\ldots, x_n]$. In this survey paper, we provide a good compilation of results on the EGH conjecture that have been obtained so far. We discuss these results in terms of their approaches.

Pete Philipson

Assessing the relative merits of sportsmen and women whose careers took place far apart in time via a suitable statistical model is a complex task as any comparison is compromised by fundamental changes to the sport and society and often handicapped by the popularity of inappropriate traditional metrics. In this work we focus on cricket and the ranking of Test match bowlers using bowling data from the first Test in 1877 onwards. A truncated, mean-parameterised Conway-Maxwell-Poisson model is developed to handle the under- and overdispersed nature of the data, which are in the form of small counts, and to extract the innate ability of individual bowlers. Inferences are made using a Bayesian approach by deploying a Markov Chain Monte Carlo algorithm to obtain parameter estimates and confidence intervals. The model offers a good fit and indicates that the commonly used bowling average is a flawed measure.

Anjalie Field, Yulia Tsvetkov

Despite their prevalence in society, social biases are difficult to identify, primarily because human judgements in this domain can be unreliable. We take an unsupervised approach to identifying gender bias against women at a comment level and present a model that can surface text likely to contain bias. Our main challenge is forcing the model to focus on signs of implicit bias, rather than other artifacts in the data. Thus, our methodology involves reducing the influence of confounds through propensity matching and adversarial learning. Our analysis shows how biased comments directed towards female politicians contain mixed criticisms, while comments directed towards other female public figures focus on appearance and sexualization. Ultimately, our work offers a way to capture subtle biases in various domains without relying on subjective human judgements.

Kaiping Chen, Anfan Chen, Jingwen Zhang et al.

This paper studies conspiracy and debunking narratives about COVID-19 origination on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substantially from those in the US. They attribute more responsibility to the US than to China, especially following Sino-US confrontations. Compared to conspiracy posts, debunking posts are associated with lower user participation but higher mobilization. Debunking narratives can be more engaging when they come from women and influencers and cite scientists. Our findings suggest that conspiracy narratives can carry highly cultural and political orientations. Correction efforts should consider political motives and identify important stakeholders to reconstruct international dialogues toward intercultural understanding.

Davor Krajnović

John Couch Adams predicted the location of Neptune in the sky, calculated the expectation of the change in the mean motion of the Moon due to the Earth's pull, and determined the origin and the orbit of the Leonids meteor shower which had puzzled astronomers for almost a thousand years. With his achievements Adams can be compared with his good friend George Stokes. Not only were they born in the same year, but were also both senior wranglers, received the Smith's Prizes and Copley medals, lived, thought and researched at Pembroke College, and shared an appreciation of Newton. On the other hand, Adams' prediction of Neptune's location had absolutely no influence on its discovery in Berlin. His lunar theory did not offer a physical explanation for the Moon's motion. The origin of the Leonids was explained by others before him. Adams refused a knighthood and an appointment as Astronomer Royal. He was reluctant and slow to publish, but loved to derive the values of logarithms to 263 decimal places. The maths and calculations at which he so excelled mark one of the high points of celestial mechanics, but are rarely taught nowadays in undergraduate courses. The differences and similarities between Adams and Stokes could not be more striking. This volume attests to the lasting legacy of Stokes' scientific work. What is then Adams' legacy? In this contribution I will outline Adams' life, instances when Stokes' and Adams' lives touched the most, his scientific achievements and a usually overlooked legacy: female higher education and support of a woman astronomer.

Andrew A. Borkowski, Catherine P. Wilson, Steven A. Borkowski et al.

Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.

Hadi Mansourifar, Weidong Shi

Breast cancer is the most frequently reported cancer type among the women around the globe and beyond that it has the second highest female fatality rate among all cancer types. Despite all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still unsatisfactory. In this paper, we propose a novel type of perceptron called L-Perceptron which outperforms all the previous supervised learning methods by reaching 97.42 \% and 98.73 \% in terms of accuracy and sensitivity, respectively in Wisconsin Breast Cancer dataset. Experimental results on Haberman's Breast Cancer Survival dataset, show the superiority of proposed method by reaching 75.18 \% and 83.86 \% in terms of accuracy and F1 score, respectively. The results are the best reported ones obtained in 10-fold cross validation in absence of any preprocessing or feature selection.

Bruno Santos, Heleno Bolfarine

In this work we show a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero, where these observations are believed to be left censored or true zeros. We combine the information provided by the quantile regression analysis to present a more complete description of the probability of being censored given that the observed value is equal to zero, while also studying the conditional quantiles of the continuous part. We build up an Markov Chain Monte Carlo method from related models in the literature to obtain samples from the posterior distribution. We demonstrate the suitability of the model to analyze this censoring probability with a simulated example and two applications with real data. The first is a well known dataset from the econometrics literature about women labor in Britain and the second considers the statistical analysis of expenditures with durable goods, considering information from Brazil.

Julián Monge-Nájera, Karla Vega Corrales

There is a marked lack of literature on user-submitted sexual videos from Latin America. To start filling that gap, we present a formal statistical testing of several hypotheses about the characteristics of 214 videos from Nereliatube.com posted from the inauguration of the site until December 2010. We found that in most cases the video was made consensually and the camera was operated by the man. The most frequent practice shown was fellatio, followed by vaginal penetration. The great majority of videos showed the sexual interactions of one woman with one man; group sex was rare. Violence and manifestations of power were rare and when there was violence it was mostly simulated. Latin American user-submitted sexual videos in Nereliatube generally reflect a society in which women and men have a variety of sexual practices that are mostly consensual and that do not differ from the biologically and anthropologically expected patterns.

Lu Xiaoming, Fan Zhaozhi

Quantile regression is a powerful statistical methodology that complements the classical linear regression by examining how covariates influence the location, scale, and shape of the entire response distribution and offering a global view of the statistical landscape. In this paper we propose a new quantile regression model for longitudinal data. The proposed approach incorporates the correlation structure between repeated measures to enhance the efficiency of the inference. In order to use the Newton-Raphson iteration method to obtain convergent estimates, the estimating functions are redefined as smoothed functions which are differentiable with respect to regression parameters. Our proposed method for quantile regression provides consistent estimates with asymptotically normal distributions. Simulation studies are carried out to evaluate the performance of the proposed method. As an illustration, the proposed method was applied to a real-life data that contains self-reported labor pain for women in two groups.

Jevin D. West, Jennifer Jacquet, Molly M. King et al.

Gender disparities appear to be decreasing in academia according to a number of metrics, such as grant funding, hiring, acceptance at scholarly journals, and productivity, and it might be tempting to think that gender inequity will soon be a problem of the past. However, a large-scale analysis based on over eight million papers across the natural sciences, social sciences, and humanities re- reveals a number of understated and persistent ways in which gender inequities remain. For instance, even where raw publication counts seem to be equal between genders, close inspection reveals that, in certain fields, men predominate in the prestigious first and last author positions. Moreover, women are significantly underrepresented as authors of single-authored papers. Academics should be aware of the subtle ways that gender disparities can appear in scholarly authorship.