Do Ultimate Fighting Championship fans exhibit gender bias Evidence from memorabilia markets

Distinguishing sexual harassment from discrimination: a factor-analytic study of residents' reports.

This study aims to explore the application of large-scale language models in detecting and reducing gender and age biases in job advertisements. To establish gender and age bias detectors, we trained and tested various large-scale language models, including RoBERTa, ALBERT, and GPT-2, and found that RoBERTa performed the best in detecting gender and age biases. Our analysis based on these models revealed significant male bias in job ads, particularly in the information and communication technology, manufacturing, transportation and logistics, and services industries. Similarly, research on age bias revealed a preference for younger applicants, with limited demand for older candidates in job ads. Furthermore, we explored the application of natural language generation using ChatGPT to mitigate gender bias in job advertisements. We generated two versions of job ads: one adhering to gender-neutral language principles and the other intentionally incorporating feminizing language. Through user research, we evaluated the effectiveness of these versions in attracting female candidates and reducing gender bias. The results demonstrated significant improvements in attracting female candidates and reducing gender bias for both versions. Overall, gender bias was reduced, and the appeal of job ads to female candidates was enhanced. The contributions of this study include an in-depth analysis of gender and age biases in job advertisements in Australia, the development of gender and age bias detectors utilizing large-scale language models, and the exploration of natural language generation methods based on ChatGPT to mitigate gender bias. By addressing these biases, we contribute to the creation of a more inclusive and equitable job market.

Medical students' accounts of gender bias in their patient care learning experiences remain limited. This study examines students' responses to gender bias and their consideration for how to prepare for gender bias in their futures. We analyzed reflective writings of third-year clinical students. Within a phenomenology framework, conventional content analysis was used to inductively analyze all essays, using HyperResearch software. We coded in teams and reconciled disagreements, then combined codes in categories to identify themes. Sixty-seven students (39 females; 28 males) wrote about gender bias in patient encounters. We identified five themes: bias, context, students' responses to patients' bias, patient-centered approach, and preparation for future encounters. Observations of bias addressed gender and structural bias, sexism, and racism. Students reflected on how context framed their experiences. Students aimed for patient-centered care, while simultaneously feeling ambivalence regarding patients' expressed bias. Students described their need to prepare for future experiences of gender bias and their plans to use specific strategies to cope with this bias. Our study offers the voices of medical students regarding gender bias in their clinical learning, presenting an important perspective, given an often-hierarchical system of medical education. This examination, which includes recommendations for curricula and policies, informs education leaders of the need to incorporate preparation for dealing with gender bias and to help students personally as they face challenging encounters with patients and medical teams. Enlightened by critical theories, these findings should also motivate resident and faculty development and promote critical inquiry for institutional changes.

BackgroundPrevious studies that have focused on the challenges faced by female surgeons, such as the gender pay gap, gender biases, lower likelihood of promotion, and gender differences in the perception of discrimination against women, are reviewed. A more comprehensive understanding of explicit and implicit gender discrimination and experiences and perceptions of discrimination is needed. This study aims to determine the current prevalence and degree of gender discrimination in the Korean Surgical Society and to compare the experiences and perceptions of gender discrimination between male and female surgeons.MethodsWe analyzed 400 responses from a survey sent to all members of the Korean Surgical Society. This electronic survey included 16 items on experiences of gender discrimination and 17 items on perceptions of gender discrimination. We conducted χ2 tests and binary logistic regression on the data regarding these experiences and perceptions of gender discrimination.ResultsAdjusted analyses found that female surgeons were more likely to experience gender discrimination than their male counterparts across all categories of discrimination. Further, adjusted analyses showed that female surgeons were more likely to confirm the presence of gender discrimination than male surgeons.ConclusionFemale surgeons were more likely to experience implicit and explicit gender biases and discrimination throughout all stages of their career progression. We also discovered significant gender differences in the perception of gender discrimination, as well as the experience of it. Changing the male-dominated culture and raising awareness of gender biases and discrimination among male surgeons are crucial steps toward addressing gender discrimination in surgery.

 Gender inequality is a sociological problem because of unfair treatment in society between various genders. These dissimilarities are distinguished from biological factors, especially from differing reproductive roles inlife. Attitude and perception toward gender discrimination are social phenomena. In this research paper researcher find out the attitude and perception of students towards gender discrimination and inequality. Descriptive Research (Survey method) applies to describe and interpret the study attitude and perception. UG and PG level students who are studying in District Bijnor will constitute the population of the study. For this analysis, mean, SD, t-test and ANOVA inferential statistics use for this research. The research used the Stratified Random Sampling technique employed for the selection of 200 student sample. According to the data analysis, all null hypotheses are rejected and the researcher finds out gender, area and subject of the stream are not affected by the attitude and perception of students. Gender discrimination and inequality is a sociological factor that society decides. How to cite this article: Vashishtha D. An Analytical Survey of Attitude and Perception towards Gender Inequality and Discrimination of Higher Education Students. J Adv Res Humani Social Sci 2022; 9(1): 1-5. DOI: https://doi.org/10.24321/2349.2872.202201

Although gender discrimination remains a feature of working life in many contexts, research on gender in organizations has shown that workplaces are often constructed as gender neutral. This poses an ideological dilemma for workers: how can they make sense of gender discrimination at work while presenting their workplace as gender neutral? This article explores that dilemma through an analysis of how information communication technology (ICT) workers talk about gender discrimination. Instead of denying gender discrimination, workers acknowledge it can happen but construct it as singular events that happened in the past and they place the onus on women to overcome such obstacles. Navigating the ideological dilemma around gender neutrality and discrimination, interviewees display what the article characterizes as gender fatigue. Copyright © 2009 ASAC. Published by John Wiley & Sons, Ltd.

Empirical research has highlighted the persistence of gender bias over centuries despite the global initiatives to promote gender equality and remove gender bias in various spheres including education. The contents of educational materials like textbooks can exert a crucial role in shaping and reinforcing the learners’ views and perceptions especially about gender roles. Addressing gender bias in textbooks is necessary in fostering a more equitable educational environment equitable, hence, promoting equitable society for future generations. The present study examined gender bias in three ELT Indonesian textbooks for three levels of education (When English Rings a Bell for Grade VII, When English Rings a Bell for Grade VIII, and Think Globally and Act Locally for Grade IX). Specifically, it investigated the distribution, types and the representation of gender bias in these textbooks. It employed both quantitative and qualitative methods of analysis to provide a more comprehensive outcomes on the gender bias presented in the textbooks. The findings indicate that gender bias is present in the three textbooks despite the urge by the Directorate General of Legislation and Regulations (2016) that textbooks must be devoid of gender stereotypes, bias, and discrimination. The portrayal of gender in the textbooks reinforces stereotype social constructs where men are depicted in leading and active roles, while women are usually shown in passive or secondary positions. This can affect the learners’ conscious and subconscious views on their own attitude, career choice and social behaviour. To develop educational materials that are free of gender-based stereotypes and reflect balanced representations of both genders, concerted effort from policy makers, educators, authors and publishers are important.

Objective: The present study aimed to characterize the association of perceived gender discrimination and illicit drug use among a sample of African American (AA) and European American (EA) adolescent girls and young women. Method: Data were drawn from a high-risk family study of alcohol use disorder of mothers and their offspring (N = 735). Multinomial regressions were used to examine whether experience of offspring and maternal gender discrimination were associated with offspring illicit drug use (cannabis, cocaine, ecstasy, PCP, opiates, hallucinogens, solvents, sedatives, or inhalants). Outcomes included offspring age of drug use initiation (age ≤ 14) and lifetime heavy drug use (≥ 50 times) of 1 or more illicit substances. Interactions between race and offspring gender discrimination were modeled to assess for race differences. Results: Results revealed that gender discrimination was associated with a greater likelihood of offspring early initiation (relative risk ratio [RRR] = 2.57, 95% CI [1.31, 5.03]) versus later initiation (RRR = 1.33, 95% CI [0.80, 2.24]). Offspring gender discrimination was associated with offspring heavy drug use (RRR = 2.09, 95% CI [1.07, 4.06]) and not associated with moderate/light use (RRR = 1.44, 95% CI [0.86, 2.42]), but post hoc tests revealed no significant group differences. Conclusions: Findings suggest that perceived offspring gender discrimination is associated with early drug use initiation. Gender discrimination, particularly at an early age, has a potential to cause harm, including drug use. Implementation of policies that foster environments that eliminate gender bias and discrimination at an early age should be prioritized. Gender-responsive treatment merits consideration by substance use treatment providers. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Legal pluralism refers to the coexistence of multiple legal systems within a society, with each system governing specific aspects of social relations. India being a large and diverse nation, with indigenous beliefs, cultural practices of various ethnic groups and multiple religious customs has a complex legal landscape that includes personal laws based on religion. In articles 25 and 26 of the Indian Constitution religious beliefs of individuals are safeguarded. Article 13 recognizes customs as laws in force and article 29 enshrines protection of minority group's culture. While it is commendable that the Constitution of India is an inclusive and secular document, its view of religious freedoms is idealistic. Religion when used to govern political matter or public matters is frowned upon, but the same beliefs when used to regulate personal and family matters are given legal recognition. This has led to a situation where personal laws based on religion perpetuate gender discrimination and inequality. This paper aims to explore the concept of legal pluralism in India and its impact on gender discrimination. The personal laws of various religions in India, such as Hindu, Muslim, and Christian laws, often contain provisions that discriminate against women. And the courts while applying these personal laws, often uphold discriminatory practices and norms under the guise of religious freedom and cultural diversity. However, when it comes to gender discrimination, the existence of personal laws based on religious norms within legal pluralistic systems can create significant challenges.These personal laws, which are based on religious principles and beliefs, often perpetuate gender inequality and discrimination against women. In order to achieve gender equality, it is crucial to address the discriminatory aspects of personal laws and work towards a more uniform legal framework that upholds fundamental rights and principles of gender equality. In the context of gender discrimination, legal pluralism plays a crucial role in shaping the rights and status of women as it reflects the coexistence of various sets of personal laws that may differ in their treatment of women. This research aims to delve into the impact of personal laws derived from different religions on women's rights, shedding light on the intersection of legal pluralism and gender discrimination.

Recognizing and Mitigating Gender Bias in Medical Teaching Assessments.

Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI) algorithms present promising solutions to a variety of technological and non-technological issues. Although, industry leaders are rapidly adopting these systems for anything from marketing to national defense operations, these systems are not without flaws. Recently, many of these systems are found to inherit and propagate gender and racial biases that disadvantages the minority population. In this paper, we analyze academic publications in the area of gender biases in ML and AI algorithms thus outlining different themes, mitigation and detection methods explored through research in this topic. Through a detailed analysis of N = 120 papers, we map the current research landscape on gender specific biases present in ML and AI assisted automated systems. We further point out the aspects of ML/AI gender biases research that are less explored and require more attention. Mainly we focus on the lack of user studies and inclusivity in this field of study. We also shed some light into the gender bias issue as experienced by the algorithm designers. In conclusion, in this paper we provide a holistic view of the breadth of studies conducted in the field of exploring, detecting and mitigating gender biases in ML and AI systems and, a future direction for the studies to take in order to provide a fair and accessible ML and AI systems to all users.

India’s Gender Bias in Child Population, Female Education and Growing Prosperity: 1951-2011 with Projections to 2026

Letters of recommendation are considered an important factor in the holistic ranking of orthopaedic residency applications. The standardized letter of recommendation (SLOR) was introduced in 2017 in response to the inherent bias and limited comparative value of traditional LORs. It includes standardized questions about an applicant's orthopaedic qualifications, designed to enable fair comparisons among candidates. However, disparate and inconsistent findings have made it difficult to draw meaningful conclusions from individual studies on the SLOR and narrative letter of recommendation. In this systematic review we asked: (1) What is the distribution of applicant SLOR rating among nine domains and summative scores? (2) Are there applicant characteristics associated with SLOR ratings? (3) Is there gender and racial bias in the SLOR and narrative letter of recommendation? PubMed, EBSCO, and Google Scholar electronic databases were queried on March 20, 2023, to identify all studies evaluating SLOR and narrative letter of recommendations between January 1, 2017, and March 20, 2023. Articles that evaluated orthopaedic SLOR or narrative LORs were included. Systematic reviews, case reports, duplicate studies between databases, and grey literature such as abstracts and articles on preprint servers were excluded. Eight studies including 6179 applicants and 18,987 letters of recommendation were included in the final analysis. The applicant classes ranged from years 2014 to 2020. Two reviewers independently evaluated the quality of the included studies using the Joanna Briggs Institute (JBI) tool for cross-sectional studies. The mean JBI score of included studies was 7.4 out of a maximum of 8, with higher scores indicating better quality. The primary outcome was to determine the distribution of applicant SLOR rankings in their summative score. Summative scores were separated into ranked to match, top one-third, middle one-third, lower one-third, and not a fit. Four studies reported the summative SLOR scores of applicants. Our secondary study goal was to assess applicant characteristics associated with SLOR summative scores and assess any bias present in the SLOR or narrative recommendation. Five studies compared SLOR ratings across applicant characteristics including gender, race, USMLE Step 1 score, USMLE Step 2 score, Alpha Omega Alpha (AΩA) membership, clerkship grades, and publications. Gender and racial bias were also assessed across five studies. Four studies utilized a linguistic analysis software to search letters of recommendation for differences in word category use by gender and race. Studies consistently found that a higher percentage of candidates were identified in the top percentile blocks than is mathematically possible. For example, the two studies that tallied the proportion of candidates ranking in the top 10% of applicants found that 36% and 42% were rated as being in the top 10%. Similarly, articles found more than 87% of applicants scored in the top one-third. Studies had mixed results, but in general, they suggested that AΩA status, higher Step 1 scores, and more research publications have a slightly positive association with increased SLOR scores. We found no evidence of gender bias against women, and in fact, most studies evaluating word choices found word choices for women candidates generally were stronger. Similarly, no consistent disparities were identified in word categories utilized in SLORs based on applicant race. Most notably, a mathematically impossible proportion of applicants were counted in the top percentiles in letters of recommendation. This compromises readers' abilities to differentiate candidates. Factors like AOA status and research publications displayed a modest positive correlation with SLOR scores. Gender bias against women or candidates from racial minority groups was not evident. We suggest that group SLOR authorship, with a consensus assessment by clerkship or residency directors, should be considered. We also recommend the incorporation of mean and SD scores for each letter writer (or group) alongside their letters. Furthermore, studies indicate that submitting all four SLOR letters can substantially improve an applicant's chances of success. We advise students to choose strategically and submit the maximum allowable number of SLORs.

The rise of AI-based systems has been accompanied by the belief that these systems are impartial and do not suffer from the biases that humans and older technologies express. It becomes evident, however, that gender and racial biases exist in some AI algorithms. The question is where the bias is rooted—in the training dataset or in the algorithm? Is it a linguistic issue or a broader sociological current? Works in feminist philosophy of technology and behavioral economics reveal the gender bias in AI technologies as a multi-faceted phenomenon, and the linguistic explanation as too narrow. The next step moves from the linguistic aspects to the relational ones, with postphenomenology. One of the analytical tools of this theory is the “I-technology-world” formula that models our relations with technologies, and through them—with the world. Realizing that AI technologies give rise to new types of relations in which the technology has an “enhanced technological intentionality”, a new formula is suggested: “I-algorithm-dataset.” In the third part of the article, four types of solutions to the gender bias in AI are reviewed: ignoring any reference to gender, revealing the considerations that led the algorithm to decide, designing algorithms that are not biased, or lastly, involving humans in the process. In order to avoid gender bias, we can recall a feminist basic understanding—visibility matters. Users and developers should be aware of the possibility of gender and racial biases, and try to avoid them, bypass them, or exterminates them altogether.

Research at the interface of operations management (OM) and gender bias has mostly focused on operational outcomes such as hiring decisions on behalf of the employer (or firm). Largely overlooked is how the design of operational processes exacerbates (or diminishes) the amount of gender bias exhibited on behalf of the customer in a people‐centric operations environment. In this study, we conduct a randomized field experiment with a partner firm to assess gender mismatch and bias in client‐consultant exchanges. The experimental design enables us to examine gender bias within dyadic exchanges when there are gender matches (female client‐female consultant or male client‐male consultant) or gender mismatches (female client‐male consultant or male client‐female consultant). We find that reporting the consultant's gender significantly increases the client's likelihood of leaving more and higher reviews, increases the clickthrough rate on recommended products, and that the effect is stronger for females than for male consultants. We also provide support for the heterogenous effects of client experience depending on the gender (mis)match in client‐consultant exchanges, including whether the prior effects hold when there is gender masking or manipulation (e.g., reported female consultant when actually male). Our findings offer important theoretical contributions and practical implications for OM scholars and managers.