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
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 âremediating,â keeps producing the same â colonial, oppressive, and tyrannical â 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 â research as reparation â 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 âCall to Actionâ 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 judgesâ 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
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
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Antecedents of business intelligence system use
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees.
This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwaitâs telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis.
The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI userâs self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture.
The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employeesâ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUTâs facilitating conditions and the limited variance of respondent demographics
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An Agile Musicology: Improvisation in Corporate Management and Lean Startups
The last decade of the twentieth century saw a proliferation of publications that use jazz as a metaphor for corporate management, arguing that in the contemporary knowledge economy, jazz is superior to the symphonic model that governed mid-century factory floors. As the literature on the jazz metaphor, and organizational improvisation more broadly, continued to develop into the twenty-first century, another managerial methodology became widely adopted by entrepreneurs: agile. While agile is yet to be fully theorized as an improvisatory practice, agile shares several core tenets with the models promoted by organizational improvisation scholars, including the use of small teams, an emphasis on feedback, and an openness to change. In this dissertation, I argue that agile methods, and the adjacent lean methodology, are inherently improvisatory and that understanding them as improvisatory offers opportunities not only for their deployment within growing businesses, but also for adoption at-scale in large corporations.
I draw on an array of disciplinary perspectives, including management science, organizational studies, musicology, and critical improvisation studies, as well as a wide range of sources, from peer-reviewed journal publications to trade manuals. Each chapter builds upon the former: a substantial and critical review of the jazz metaphor literature is followed by a dissection of its main themes under a musicological lens; after securing the foundations of organizational improvisation, the next chapter reveals the improvisatory nature of agile and lean startup practices and links them to concepts discussed within the jazz metaphor literature. Drawing on insights from large-scale improvisatory musical practices, the final chapter reveals how improvisation, as a set of practices shared between corporate management and agile methodologies, provides avenues for agile to be scaled up as startups grow or for its widespread adoption within established companies
Differential Models, Numerical Simulations and Applications
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
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 âTechnologies and Methodsâ contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part âProcesses and Applicationsâ 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
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
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 userâs 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 participantâs awareness of being recorded (which otherwise is known to affect the naturalness of oneâs 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 âEvaluator Weighted Estimationâ 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}/Ï_{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
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