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Can You Be a Catholic and a Feminist?
No full textAn eminent theologian addresses an enduring--but newly urgent--questionIs it possible to be both a faithful Catholic and an avowed feminist? Earlier generations of feminists first formulated answers to this question in the 1970s. Their views are still broadly held, but with increasing tentativeness and a growing sense of their inadequacy. Even now, Catholic women and men still say, It\u27s my Church and I\u27m not leaving, Change will only happen if people like me stay and fight, and The Church\u27s work for social justice is more important than the issues that concern me as a feminist. Yet in a post-#MeToo, #ChurchToo moment, when the Church seems disconnected from struggles for racial justice and LGBTQ inclusion, those answers sound increasingly insufficient. Today, tensions between Catholicism and feminism are more visible and ties to Catholic communities are increasingly weak. Can Catholic feminism survive?Julie Hanlon Rubio argues that it can. But if it is going to do so, it is necessary to rethink how women and men who experience the pull of feminism and Catholicism can credibly claim both identities. In Can You Be a Catholic and a Feminist? Rubio argues that Catholic feminist identity is only tenable if we frankly acknowledge tensions between Catholicism and feminism, bring forward shared concerns, and embrace the future with ambiguity and creativity. Rubio explores the potential for synergy and dialogue between Catholics and feminists through various lenses, including sexual violence, gender theory, pregnancy and pre-natal loss, work-life balance, relationships and family life, spirituality, conscience, and what it means to be human. This book gives those who struggle to balance Catholicism and feminism a credible path to authentic belonging.https://scholarcommons.scu.edu/faculty_books/1634/thumbnail.jp
Picture Bride, War Bride: The Role of Marriage in Shaping Japanese America
No full textExamines the role marriage played in the lives of Japanese women during periods of racial exclusion in the United StatesIn 1908 the United States and Japan agreed to limit the migration of Japanese laborers to the US. The Gentlemen’s Agreement of 1908 ushered in an era of exclusion for the Japanese, but an exception was made for Japanese women who migrated as wives of Japanese men. In 1924 that exception would end with the passage of the National Origins Act. Immediately after World War II, Japanese women were once again permitted to enter the US as brides— this time, however, as the wives of American servicemen stationed throughout Japan. The ban on Japanese immigration would not be lifted until 1952.Picture Bride, War Bride examines how the institution of marriage created pockets of legal and social inclusion for Japanese women during the period of Japanese exclusion. Sonia C. Gomez begins with the first wave of Japanese women\u27s migration in the early twentieth century (picture brides), and ends with the second mass migration of Japanese women after World War II (war brides), to illustrate how popular and political discourse drew on overlapping and conflicting logics to either racially exclude the Japanese or facilitate their inclusion via immigration legislation privileging wives and mothers. Picture Bride, War Bride retells the history of Japanese migration and exclusion by centering women, gender, and sexuality, and in so doing, troubles the inclusion versus exclusion binary. While the Japanese were racially excluded between 1908 and 1952, Japanese wives and mothers were permitted entry because their inclusion served American interests in the Pacific. However, the very rationale enabling their inclusion simultaneously restricted and defined the parameters of their lives within the US.Picture Bride, War Bride serves as a compelling analysis of how the intricate interplay between societal norms and political interests can both harness and contradict the interconnected frameworks of race, gender, and sexuality.https://scholarcommons.scu.edu/faculty_books/1635/thumbnail.jp
Geo Spaces of Communication Research
No full textSponsored by the Brazil-U.S. Colloquium on Communication Studies of the Brazilian Society for Interdisciplinary Studies in Communication and the Communication, Information Technologies, and Media Sociology Section of the American Sociological Association (CITAMS), this volume of Studies in Media and Communications is entitled Geo Spaces of Communication Research.
The volume brings together scholars from across the Americas to address the complex evolution of political and policy media spaces as they are studied from a range of perspectives. The volume probes how media and digital tech are transforming how individuals, groups, and societies communicate within and across social worlds, as well as how emergent methodologies are evolving to keep pace with these phenomena.https://scholarcommons.scu.edu/faculty_books/1639/thumbnail.jp
Algorithmic Aspects in Information and Management: 18Th International Conference, AAIM 2024, Virtual Event, September 21-23, 2024, Proceedings. Part II
No full textThis two-volume set LNCS 15179-15180 constitutes the refereed proceedings of the 18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024, which took place virtually during September 21-23, 2024.The 45 full papers presented in these two volumes were carefully reviewed and selected from 76 submissions. The papers are organized in the following topical sections:Part I: Optimization and applications; submodularity, management and others,Part II: Graphs and networks; quantum and others.https://scholarcommons.scu.edu/faculty_books/1644/thumbnail.jp
Examining Transformational Resistance Through Asiancrit-Informed Counterstories: The Impact of Perceived Covid-19 Related Anti-Asian Hate on Pathways to Liberation
No full textThe COVID-19 global pandemic paired with the open access to information through countless media outlets brought a resurgence of yellow peril. This research study explores the intersection of model minority notions and anti-Asian hate. Through shared storytelling of multiple generations, this study demonstrates the continued struggle that Chinese Americans navigate as perpetual foreigners as they seek opportunities for greater voice and agency in schools and in their communities
Leveraging Green Financial Instruments to Bridge the Decarbonization Gap in Minority-owned SMEs in Santa Clara
No full textThe world of financing is constantly evolving to respond to the needs and trends of the markets. In recent years, this evolution has been illustrated in the more significant emergence of financial solutions supported by technology and the important advances that have sought to align financing mechanisms with the Sustainable Development Goals (SDGs) promoted by the United Nations and the Paris Agreement. Public and private entities have adopted these guidelines in a growing dynamic at a global level.
As of 2020, minority-owned businesses comprise approximately 30% of California’s 4.1 million small businesses (Figure 1). In Santa Clara, minority-owned businesses receive only 15% of the total payments from Santa Clara’s contracts. The city’s Vendor Disparity Study, covering July 2016 to June 2021, revealed that of the 364 million went to minority-owned businesses. This includes less than 1% spent on Black, Hispanic, or Native American-owned businesses
Individual and cultural differences in compassion, noticing suffering, and well-being: Consequences of wanting to avoid feeling negative
No full textAlthough people across the globe experience suffering, some individuals find it difficult to respond to others\u27 distress because they might not know what would be most helpful to that person in that particular situation. For instance, would a response that focuses on the silver lining or one that acknowledges the suffering of the other person be more compassionate and helpful? In this paper, we demonstrate that the construct of avoided negative affect (ANA), the degree to which people want to avoid feeling negative, can predict individual differences and explain cultural variations in several aspects of compassion. Because ANA is a relatively new construct, we review research that focuses on concepts that are related to, but differ from ANA. Then, we summarize individual and cultural differences in ANA and their consequences for expressions of sympathy and compassion, conceptualizations of compassion, noticing suffering (including acknowledging suffering such as systemic racism), and well-being. Across many studies conducted in various cultural contexts including Ecuador, Mexico, China, Japan, Germany, and the United States, ANA can partly explain cultural differences in different aspects of compassion. This work has important implications for cross-cultural counseling, anti-racism trainings, and conflict resolution. Noticing others\u27 suffering and understanding what compassion entails for different people in different settings can result in treating others the way they want to be treated
If I Cannot Hear Us, How Do I Know We Are Here? A journey into Assyrian storytelling
Get PDFIn this collection of poetry and prose, Nadine Koochou explores her Assyrian-American identity, retracing family history and merging it with her own reflections. Through creative essays, she communicates the things she knows, to some degree of certainty. Through poetry, she communicates things she thinks she knows, or feelings she understands intrinsically, or things she does not know at all but wishes to know. Combining Assyrian history with intimate stories, she explores themes of generational trauma, language, love, war, and home
Enhanced Adaptive Image-Codebook Learning for Image Reconstruction
Get PDFIn the field of image reconstruction and super-resolution, using codebooks has shown promising results despite various image degradations. Previous methods either use distinct codebooks for each image category or multiple codebooks per category, with the latter achieving better performance by capturing more nuanced image features. Our research proposes a novel method that employs enhanced sets of codebooks and weight maps tailored to each image category. These weight maps dynamically combine different codebook bases to adapt to various reconstruction tasks, resulting in improved image recognition and robustness. This approach significantly enhances the expressiveness and quality of reconstructed images, making it versatile and effective for handling diverse image degradation scenarios
Deep Learning-Based Video Prediction
Get PDFThe task of video prediction is to generate unseen future video frames based on the past ones. It is an emerging, yet challenging task due to its inherent uncertainty and complex spatiotemporal dynamics. The ability to predict and anticipate future events from video prediction has applications in various prediction systems like self-driving cars, weather forecasting, traffic flow prediction, video compression etc. Due to the success of deep learning in the computer vision field, several deep learning Artificial Intelligence (AI) architectures such as convolutional neural networks (CNNs), long short-term memory (LSTMs), convolutional LSTMS (ConvLSTMs) and transformers have been explored to improve prediction accuracy. The internal representation, mainly the spatial correlations and temporal dynamics of the video, is learned and used to predict the next frames in deep learning-based video prediction. Several state-of-the-art deep learning methods have achieved superior video prediction accuracy at the expense of huge computational cost. In the light of recent wide popularity of Green AI which aims for efficient environment friendly solutions alongside accuracy, this research concentrates on efficient methods for video prediction. Such methods are suitable for memory-constrained and computation resource-limited platforms, such as mobile and embedded devices. We focus on CNN/LSTM methods and transformer-based architectures with fewer parameters for our lightweight efficient environment-friendly video prediction techniques. We conducted experimental studies on popular video prediction datasets and compared to existing methods, our proposed methods achieved competitive frame prediction accuracy with significantly reduced model size, trainable parameters, and computational complexity