8,344 research outputs found

    Towards ending incarceration of Indigenous peoples in Canada: A critical, narrative inquiry of hegemonic power in the Gladue report process

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    Abstract This study is concerned with the possibility that Gladue perpetuates the hegemonic powers of settler colonialism, white supremacy, patriarchy, and neoliberalism. Gladue is intended to remediate systemic anti-Indigenous racism by requiring judges to consider all alternatives to incarceration when sentencing Indigenous peoples, yet Indigenous incarceration rates continue to rise precipitously. On the surface, Gladue does not appear to disrupt the hegemonic status quo. How is it that the Canadian state, even when ‘remediating,’ keeps producing the same – colonial, oppressive, and tyrannical – result? This qualitative study used a critical, narrative methodology, interviewing Gladue report writers (n=9) and judges (n=12) about their perspectives and experiences with Gladue, particularly Gladue reports. The study purposefully emphasized settler accountability – research as reparation – in the research design, data collection, and analysis. A careful, ethical protocol for researching with Indigenous peoples (n=9) was followed, premised in Truth and Reconciliation ‘Call to Action’ number 30 to reduce Indigenous incarceration in Canada. This study found that Gladue is falling short of achieving its systemic aim because of (a) a hyper-individualistic, dehumanizing configuration that discursively shifts judges away from dealing with the systemic issue of anti-Indigenous racism, towards judging the individual Indigenous person before the court; (b) colonial mentalities (e.g., whiteness and patriarchy) persisting in the process; (c) a lack of funding for Gladue writers, as well alternatives to incarceration, constraining judges’ capacities to divert Indigenous away from prisons. The study points towards the need for a more radical framework for Gladue that honours Indigenous self-determination and foundational treaties such as the Two Row Wampum

    A Phenomenological Study of How Active Engagement in Black Greek Letter Sororities Influences Christian Members\u27 Spiritual Growth

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    This phenomenological study explored how being part of a Black Greek Letter. Organization (BGLO) sorority impacts the spiritual growth of its Christian members. One of the issues explored was the influence relationships within these sororities have on members striving to be like Christ. There is a dichotomy of perspectives regarding Black Greek Letter Organizations (BGLOs). They have a significant role in the Black community as organizations that foster leadership, philanthropy, and sisterhood and promote education. They are admired on and off college campuses and in the broader community in graduate chapters. The objective of phenomenology is to describe phenomena of spiritual growth among Christian sorority members from the life experiences of those who live them; that premise guided the interviews conducted for this study. The results found that active engagement in a BGLO sorority positively impacts its members\u27 spiritual growth. From the emotional stories of sisterhood, service, and devotion to prayer, their experiences evidenced strengthened walks of faith. This study contrasts the Anti-BGLO narrative as a testament to these organizations\u27 legacy and practices deeply grounded in the church

    Differential Models, Numerical Simulations and Applications

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    This Special Issue includes 12 high-quality articles containing original research findings in the fields of differential and integro-differential models, numerical methods and efficient algorithms for parameter estimation in inverse problems, with applications to biology, biomedicine, land degradation, traffic flows problems, and manufacturing systems

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Memory and Identity in the Learned World

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    Accounts and analyses of the formation of scholarly and scientific communities in the early modern period by means of memory and collective identity

    Time- and value-continuous explainable affect estimation in-the-wild

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    Today, the relevance of Affective Computing, i.e., of making computers recognise and simulate human emotions, cannot be overstated. All technology giants (from manufacturers of laptops to mobile phones to smart speakers) are in a fierce competition to make their devices understand not only what is being said, but also how it is being said to recognise user’s emotions. The goals have evolved from predicting the basic emotions (e.g., happy, sad) to now the more nuanced affective states (e.g., relaxed, bored) real-time. The databases used in such research too have evolved, from earlier featuring the acted behaviours to now spontaneous behaviours. There is a more powerful shift lately, called in-the-wild affect recognition, i.e., taking the research out of the laboratory, into the uncontrolled real-world. This thesis discusses, for the very first time, affect recognition for two unique in-the-wild audiovisual databases, GRAS2 and SEWA. The GRAS2 is the only database till date with time- and value-continuous affect annotations for Labov effect-free affective behaviours, i.e., without the participant’s awareness of being recorded (which otherwise is known to affect the naturalness of one’s affective behaviour). The SEWA features participants from six different cultural backgrounds, conversing using a video-calling platform. Thus, SEWA features in-the-wild recordings further corrupted by unpredictable artifacts, such as the network-induced delays, frame-freezing and echoes. The two databases present a unique opportunity to study time- and value-continuous affect estimation that is truly in-the-wild. A novel ‘Evaluator Weighted Estimation’ formulation is proposed to generate a gold standard sequence from several annotations. An illustration is presented demonstrating that the moving bag-of-words (BoW) representation better preserves the temporal context of the features, yet remaining more robust against the outliers compared to other statistical summaries, e.g., moving average. A novel, data-independent randomised codebook is proposed for the BoW representation; especially useful for cross-corpus model generalisation testing when the feature-spaces of the databases differ drastically. Various deep learning models and support vector regressors are used to predict affect dimensions time- and value-continuously. Better generalisability of the models trained on GRAS2 , despite the smaller training size, makes a strong case for the collection and use of Labov effect-free data. A further foundational contribution is the discovery of the missing many-to-many mapping between the mean square error (MSE) and the concordance correlation coefficient (CCC), i.e., between two of the most popular utility functions till date. The newly invented cost function |MSE_{XY}/σ_{XY}| has been evaluated in the experiments aimed at demystifying the inner workings of a well-performing, simple, low-cost neural network effectively utilising the BoW text features. Also proposed herein is the shallowest-possible convolutional neural network (CNN) that uses the facial action unit (FAU) features. The CNN exploits sequential context, but unlike RNNs, also inherently allows data- and process-parallelism. Interestingly, for the most part, these white-box AI models have shown to utilise the provided features consistent with the human perception of emotion expression

    The Anthropocene Hypothesis

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