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<p><em>This thesis applies a Bourdieuian analysis to a qualitative study of volunteerism that focuses on the question of how ‘doing good’ relates to social change overall? - in particular, the ways that social class influences the volunteer experience and the overall culture of volunteerism. I argue that the leading theoretical research models of volunteerism need to recognize the evolving nature of the activity and theorize the influences that are structuring the culture of volunteerism. What emerges is a model of volunteering where motivation is understood as a complex set of factors that are structured by social class identities and volunteering is understood as a form of distinction that can be used to acquire cultural capital. Social class-based ideas, in particular, the values related to the middle class, have become a part of the culture of volunteerism and, in part, create and reproduce the social change/volunteerism paradox. The volunteerism/social change paradox is the idea that volunteerism is often perceived as a social change activity when in many cases it reproduced the status quo. Without a strong activism component to the volunteerism, it is not an inequality challenging activity. </em></p> <p><em>This thesis presents interview and observation data collected with Habitat for Humanity Canada in their two largest programs – the National program (domestic) and the Global Village program (international). By utilizing ideas of class, class distinction and social and cultural capital from Bourdieu’s work, the role of class, the culture of benevolence (or volunteerism) can be explored in a new way. What emerges is a culture of volunteerism that is deeply influenced by middle class values where social change ideas are common but structural change is not – resulting in the volunteer/social change paradox.</em></p> / Doctor of Philosophy (PhD)

<p>This work explores the process of social change through an examination of the interaction between the Canadian state and the women's movement at the macro, mezzo and micro level concerning the issue of woman abuse. Using an expanding case study, an analysis of a small family service agency, a medium size Canadian city and the parliamentary and public hearing process are combined to examine how feminist challenges at various levels have interacted to transform the response of the state to woman abuse.</p> <p>In addition, this work examines the effectiveness of various feminist approaches to creating change. I challenge the dichotomy between mainstreaming and disengagement (Adamson et al ,1988) arguing that when it comes to effective action it is not a question of 'either/or' but rather 'which one, when'. The conclusion of the work is that challenge is required from many locations, in many different forms if change is to occur.</p> / Doctor of Philosophy (PhD)

<p>While neoliberalism has increasingly been entrenched in the province of Ontario, it has also encountered resistance. A primary goal of this dissertation is to provide a vivid historical account of women’s organizing and feminist resistance that emerged during the Harris years (1995-2001). Drawing on the narratives of thirty three feminist activists and leaders working within three different organizational settings, namely organized labour, anti-racist, and anti-violence organizations, this research project aims to capture an important moment in time when women and feminists were speaking out against some of the most pressing issues of the day including gender (in)equality, poverty, violence, sexism, racism and discrimination. Thematically, this research highlights the numerous challenges, strategies and successes experienced by a variety of differently located feminists and women’s groups. By theoretically and analytically situating these women’s narrative accounts within a qualitative, intersectional feminist framework, this collection of stories not only allow us to (re)examine theoretical and practical issues related to intersectionality and pose questions for the present state of feminist organizations, but also contributes to our understanding of feminist organizational resistance in the future, the long-term impact of said resistance and how we might go about addressing the challenges that lie ahead.</p> / Doctor of Philosophy (PhD)

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This thesis develops an Artificial Intelligence (AI) approach intended for accurate patient stratification and precise diagnostics/prognostics in clinical and preclinical applications. The rapid advance in high throughput technologies and bioinformatics tools is still far from linking precisely the genome-phenotype interactions with the biological mechanisms that underlie pathophysiological conditions. In practice, the incomplete knowledge on individual heterogeneity in complex diseases keeps forcing clinicians to settle for surrogate endpoints and therapies based on a generic one-size-fits-all approach. The working hypothesis is that AI can add new tools to elaborate and integrate together in new features or structures the rich information now available from high-throughput omics and bioimaging data, and that such re- structured information can be applied through predictive models for the precision medicine paradigm, thus favoring the creation of safer tailored treatments for specific patient subgroups. The computational techniques in this thesis are based on the combination of dimensionality reduction methods with Deep Learning (DL) architectures to learn meaningful transformations between the input and the predictive endpoint space. The rationale is that such transformations can introduce intermediate spaces offering more succinct representations, where data from different sources are summarized. The research goal was attacked at increasing levels of complexity, starting from single input modalities (omics and bioimaging of different types and scales), to their multimodal integration. The approach also deals with the key challenges for machine learning (ML) on biomedical data, i.e. reproducibility, stability, and interpretability of the models. Along this path, the thesis contribution is thus the development of a set of specialized AI models and a core framework of three tools of general applicability: i. A Data Analysis Plan (DAP) for model selection and evaluation of classifiers on omics and imaging data to avoid selection bias. ii. The histolab Python package that standardizes the reproducible pre-processing of Whole Slide Images (WSIs), supported by automated testing and easily integrable in DL pipelines for Digital Pathology. iii. Unsupervised and dimensionality reduction techniques based on the UMAP and TDA frameworks for patient subtyping. The framework has been successfully applied on public as well as original data in precision oncology and predictive toxicology. In the clinical setting, this thesis has developed1: 1. (DAPPER) A deep learning framework for evaluation of predictive models in Digital Pathology that controls for selection bias through properly designed data partitioning schemes. 2. (RADLER) A unified deep learning framework that combines radiomics fea- tures and imaging on PET-CT images for prognostic biomarker development in head and neck squamous cell carcinoma. The mixed deep learning/radiomics approach is more accurate than using only one feature type. 3. An ML framework for automated quantification tumor infiltrating lymphocytes (TILs) in onco-immunology, validated on original pathology Neuroblastoma data of the Bambino Gesu’ Children’s Hospital, with high agreement with trained pathologists. The network-based INF pipeline, which applies machine learning models over the combination of multiple omics layers, also providing compact biomarker signatures. INF was validated on three TCGA oncogenomic datasets. In the preclinical setting the framework has been applied for: 1. Deep and machine learning algorithms to predict DILI status from gene expression (GE) data derived from cancer cell lines on the CMap Drug Safety dataset. 2. (ML4TOX) Deep Learning and Support Vector Machine models to predict potential endocrine disruption of environmental chemicals on the CERAPP dataset. 3. (PathologAI) A deep learning pipeline combining generative and convolutional models for preclinical digital pathology. Developed as an internal project within the FDA/NCTR AIRForce initiative and applied to predict necrosis on images from the TG-GATEs project, PathologAI aims to improve accuracy and reduce labor in the identification of lesions in predictive toxicology. Furthermore, GE microarray data were integrated with histology features in a unified multi-modal scheme combining imaging and omics data. The solutions were developed in collaboration with domain experts and considered promising for application.

No / Pressure variation during the coextrusion of two low density polyethylene melts was investigated. Melt streams were delivered to a die from two separate extruders to converge in a 30 degrees degrees geometry to form a two layer extrudate. Melt flow in the confluent region and die land to the die exit was observed through side windows of a visualisation cell. Stream velocity ratio was varied by control of extruder screw speeds. Layer thickness ratios producing wave type interfacial instability were quantified for each melt coextruded on itself and for the combined melts. Stream pressures and screw speeds were monitored and analysed. Wave type interfacial instability was present during the processing of the melts at specific, repeatable, stream layer ratios. Increased melt elasticity appeared to promote this type of instability. Analysis of process data indicates little correlation between perturbations in extruder screw speeds and stream pressures. The analysis did however show covariance between the individual stream pressure perturbations. Interestingly there was significant correlation even when interfacial instability was not present. We conclude that naturally occurring variation in extruder screw speeds do not perturb stream pressures and, more importantly, natural perturbations in stream pressures do not promote interfacial instability.

Past research has established a marital wage premium among men, and more recently, among women of the baby boom generation. It is unknown whether: 1) the marriage premium holds among more recent cohorts of men and women, 2) it differs by intensity of work hours among husbands and wives, and 3) cohabiters receive wage bonuses. Using fixed-effects models and data from the 1979-1989 waves of the National Longitudinal Survey of Youth (NLSY79) and the 1997-2010 waves of the National Longitudinal Survey of Youth (NLSY97), this paper compares cohort differences in the gendered marriage premium. While both women and men receive marriage premiums and these premiums are larger for more recent cohorts, men’s premiums are consistently higher and have doubled from the late baby boomers cohort (NLSY79) to the late Generation X (Gen X) cohort (NLSY97). While there was no wage premium for cohabitation among baby boom cohort women, I observe a premium among Gen X men and women. Household specialization matters: while among baby-boomers the marriage premium did not vary by household type, among the Gen X cohort men’s marriage premium is significantly larger among male breadwinner households, and surprisingly, I find marriage penalties for men in female-breadwinner households. Similarly, Gen X female breadwinners and female dual-earners receive the marriage premium while Gen X women in male-breadwinner households experience marriage penalty. In addition, the more highly educated receive larger marital bonuses.