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The Rise Of Null Hypothesis Significance Testing As The Gold Standard In Psychology, 1940-55
No full textIn the midst of the ongoing replication crisis, it is more important than ever for psychologists to look critically at methodology. Nearly every psychologist uses null hypothesis significance testing (NHST) to analyze and draw conclusions from quantitative data, despite its imperfections and the heavy criticisms it has received, particularly in recent years. Though NHST is usually taught as a strictly formalized, “objective” procedure inherent to the field, it was only introduced to psychology in the 1930s. The method reached its current status of ubiquity during the so-called “inference revolution” of 1940-55 (Gigerenzer & Murray, 1987). However, the means through which this revolution occurred remain unclear. By reviewing course catalogues from six major Canadian universities from 1940-55 and situating my findings in the broader historical context, I aim to disentangle how, and why, NHST became the gold standard of psychological statistics during this time
AI-based Assistive Technologies & People With Disabilities: Privacy At Risk
No full textThis thesis follows three research questions. First, it explores the potential privacy risks that people with disabilities (PWDs) face in the face of incorporation of artificial intelligence (AI) in assistive technologies (ATs). It then investigates reasons that exacerbate PWDs’ vulnerability to such potential privacy risks. Since legal literature in the context of AI-based ATs is limited, this thesis adopts a combination of multidisciplinary and traditional legal doctrinal research by studying legal literature and empirical research in other disciplines. Lastly, the thesis reviews the current Canadian data protection legal framework to examine if there is any provision specifically addressing PWDs or vulnerable data subjects and highlight legislative gaps impacting PWDs
Modeling of Eye contact behavior
No full textWith the rise of online platforms and avatar-based communication, understanding eye contact a key non-verbal cue is crucial for trust in conversations. This study examines eye contact behavior across face-to-face interactions, a screen-sized window interface, and online meetings.
We collected twelve hours of eye contact data from 48 individuals using eye trackers and motion capture in dyadic settings. Our analysis showed consistent eye contact patterns in face-to-face and screen-sized window interactions, while online meetings caused significant shifts due to the lack of direct eye contact.
To model this behavior, we trained a diffusion model (DDPM) to generate synthetic eye movements that preserved key features of real data. We evaluated our model using metrics such as eye contact frequency. This study provides insights into how communication media influence gaze behavior and explores methods for generating realistic eye movements in conversational settings
Black Girls Need Love Too: An intersectional analysis of the lived experiences of Black girls in French Immersion Programs
No full textBlack girls in the French immersion program are silent scholars. They continue to remain under the radar, significantly under-researched and hidden behind the generalization of Black students without considerations for the intersectionality of race and gender. Using the narratives of four Black girls through semi-structured interviews, this thesis addresses this gap by exploring the lived experiences of four Black girls in Southern Ontario’s French immersion programs. By thematically analyzing their experiences using Black Canadian feminism, raciolinguistics, and intersectionality, we can critically assess their experiences and provide strategies to counter colonial institutions and policies. The findings reveal insights into their identities, sense of belonging, representation, and treatment, and how these themes impact their educational experiences. The experiences of these Black girls highlight several key areas for improvement and offer opportunities to enhance educational experiences for Black girls by informing more equitable policies and practices in the French immersion program
The Effect Of Ketogenic Diet On Hepatic Cholesterol Metabolism
No full textThe ketogenic diet (KD), known for its high-fat, low-carbohydrate composition, has gained popularity for weight loss and metabolic health benefits. Despite these advantages, there are concerns that the diet's high saturated fat content might elevate cholesterol levels and cardiovascular disease (CVD) risk. This study investigates the KD's impact on the molecular mechanisms of cholesterol metabolism in the liver, focusing on cholesterol synthesis markers such as 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase) and sterol regulatory element-binding protein-2 (SREBP-2), as well as cholesterol uptake markers including proprotein convertase subtilisin/kexin type 9 (PCSK9) and LDL receptors (LDLr). For that, male Wistar rats (n = 6 per group) were fed for 16 weeks one of the following diets: standard chow (SC, 60% carbohydrates, 13% fat, 27% protein), high-fat sucrose-enriched (HFS, 20% carbohydrates, 60% fat, 20% protein), and ketogenic diet (KD, 0% carbohydrates, 80% fat, 20% protein). Liver tissue was extracted and analyzed for gene expression using real-time PCR and protein content using western blotting. Blood samples were collected to measure circulating cholesterol levels. We found that neither plasma cholesterol levels nor HMG-CoA reductase and SREBP-2 levels in the liver differed among the dietary interventions. However, the KD significantly reduced liver PCSK9 content and expression in comparison other diets, suggesting that the KD enhanced clearance of circulating cholesterol by the liver. To test whether there was a higher amount of LDLr on the membrane compared to the cytoplasm, the ratio of LDLr distribution between these compartments was measured. Importantly, there was an upward trend in the levels of LDLr on the membrane. In conclusion, the KD altered key steps that regulate hepatic cholesterol metabolism and prevented plasma cholesterol levels from increasing, despite its elevated saturated fat content
Deep Generative Models for Trajectory Prediction and Mobility Network Forecasting
No full textPredicting human mobility is essential for urban planning, traffic management, and epidemiology. This thesis tackles two intertwined challenges: accurately forecasting individual trajectories and inferring the resulting mobility network. First, we introduce TrajLearn, a Transformer‑based deep generative model that treats trajectories as token sequences and employs spatially constrained beam search to predict each individuals’s next k locations with high precision. Building on these forecasts, we present MobiNetForecast, which constructs and predicts the future topology of the mobility network by detecting when independently predicted trajectories intersect in space and time. Across large, real‑world datasets, our unified framework achieves up to 40% relative gains in trajectory accuracy and up to 100x improvement in contact prediction over state-of-the-art baselines. These results demonstrate that combining advanced sequence modeling with explicit contact inference offers a powerful, scalable solution for dynamic mobility network forecasting
The ‘Visa Student Dream’: An Examination of Shifting Trends and Vulnerabilities in Chinese International Student Populations Within Toronto’s Secondary Schools
No full textThis study aims to explore the experiences of Chinese international students in secondary schools in Canada, paying particular attention to the vulnerabilities and risks among this population. Along with the pre-existing systemic issues international students face, the COVID-19 pandemic placed these students in an even more precarious position due to the anti-Asian racism and discrimination that manifested itself during this time period. The study employed Critical Race Theory (CRT), specifically Asian Critical Theory (AsianCrit) which prioritizes Asian identity and their experiences with racism to understand and contextualize how prevailing systems of oppression impacted the lives of Chinese international students. Utilized alongside these theories are complementary frameworks like International Student Security (ISS) and neoliberalism to further explore the experiences, vulnerabilities and risks among this population. Multiple constructs were also used like model minority, yellow peril, neo-racism, and racial capitalism to expand understanding and application of theories such as CRT and AsianCrit to this international student population. The principles highlighted within CRT and AsianCrit theories, utilized alongside the frameworks and constructs all built upon each other to provide further insight into how educational institutions continue to operate within a dominant culture paradigm and with whiteness as a norm and how programs are maintained and/or implemented based on assumed notions and ideologies of Asian international students. Interview data was collected from six teachers and six Chinese international students from public secondary schools within the Greater Toronto Area (GTA). Findings revealed that neoliberalism has ultimately created unethical and unsafe policies and practices that negate EDI initiatives, and the push towards standardization and individualism perpetuate racial inequities. Standards of success are rooted in Whiteness that validates Western knowledge, with concepts like racial capitalism playing a role in exploiting Chinese international students. Issues and concerns regarding the academic, social, and housing/guardianship experiences of Chinese international youth were also revealed, as students faced discrepancies between the services and support that was advertised in comparison to what they actually experienced in Canada. Key concerns and gaps in policy and programmatic supports were also identified for international students with the study outlining recommendations and interventions to better support international secondary students during their studies in Canada
Graph Learning and Optimization for Irregular-Structured Signal Processing
No full textGraph Signal Processing (GSP) extends harmonic analysis tools, such as Fourier transforms and wavelets, to discrete signals defined on finite graphs, enabling tasks like signal denoising, prediction, and interpolation on irregular domains. A critical first step in GSP is to learn an appropriate graph that captures pairwise similarities or correlations inherent in the data, ensuring that subsequent graph-based filtering effectively leverages local structure for improved performance.
However, most existing graph learning methods assume static relationships, while real-world interactions often evolve over time. To address this problem, this thesis proposes a slowly time-varying graph learning framework that models the difference between consecutive adjacency matrices as a low-rank matrix. This approach accommodates gradual shifts in node-to-node similarities over time, enabling efficient graph updates with low computational overhead while maintaining alignment with the underlying data.
Beyond graph construction, the challenge of dense or complete graphs often arises, particularly in large-scale applications where representing all possible edges is computationally prohibitive. To address this issue, this thesis introduces a sparsification method guided by the Fiedler number, the second smallest eigenvalue of the Laplacian, which quantifies graph connectivity. By removing edges that minimally affect the Fiedler number, the resulting sparser graph preserves essential connectivity while significantly reducing training and inference costs for deep learning models (e.g, graph convolutional networks (GCNs)). Together, these contributions provide a flexible and computationally efficient approach to GSP in dynamic and large-scale graph settings
Photography and the Extractive Gaze: Visual culture and natural resource extraction on the Canadian Shield, 1900-1930
No full textWhile the Anthropocene is a relatively new concept, communities in extractive zones have already experienced the transformations, changes, and destruction brought by human activity and industrial development. Through archival research, my dissertation investigates how vernacular and survey photographs of mining from the early 20th century circulated to promote and occasionally challenge resource extraction. Taking as its case study the Timiskaming region on the Precambrian shield, I explore how photography chronicled the transformation of territory under extractive capitalism as photographs invited viewers to envision new futures, where commerce and art would come together to drive economic and industrial development. These archival photographs formed a site of knowledge production that shaped how people in the early 20th century understood industrial development. I situate contemporary experiences of climate change within these longer historical trajectories to trace the social and environmental legacies of resource extraction.
Once extracted from the earth, raw natural resources are transformed into consumer goods, which bear little evidence of the complex networks of human and non-human labour that brought them into being. Environmental art historians have addressed this disconnect by examining how human cultural production has unfolded within a broader ecological context, challenging the separation of art from the natural world. This dissertation makes a material link between silver and photography during a period where the demand for silver bullion exploded due to the rise of amateur photography. My research identifies connections between photographic technologies, visual form, and political activism. I conclude that photography can bring the often-invisible processes of extraction into view and document the historical production of environmental trauma. I read the histories that the archival photographs contain, and the histories that were foreclosed, as documents that can teach us something about hope, healing, and living in the Anthropocene
Assessing and Enhancing the Quality of News Headlines Using Machine Learning
No full textHeadlines play a pivotal role in capturing readers' attention, and their quality is critical for engaging audiences. In this thesis, we propose various solutions to assist news media in crafting high-quality headlines. First, we delve into headline quality assessment, devising four innovative indicators that automatically evaluate headlines' quality. Our proposed model empowers news outlets to automatically determine the quality of published headlines. We evaluate the quality of headlines from The Globe and Mail using these four indicators and provide insightful results. We then use this labeled data to train our novel headline quality prediction model to predict the quality of unpublished headlines, assisting journalists in selecting high-quality headlines for their articles. Furthermore, we facilitate journalists' work by recommending high-quality headlines for their articles. To accomplish this, we propose a headline generative model that learns to generate headlines using Reinforcement Learning (RL). Our model can be optimized not only with respect to a non-differentiable metric but also based on a combination of two different metrics simultaneously. Additionally, we enhance headline generation in terms of both training speed and the quality of the generated headlines by proposing a novel architecture utilizing state-of-the-art transformer models. In our architecture, after generating candidate headlines using state-of-the-art models, we select the most popular headline using our headline popularity prediction model. Moreover, we establish a popularity benchmark for evaluating headline generation models based on their ability to generate popular headlines. Lastly, we forecast changes in how people consume news articles, envisioning a shift towards interacting with agents instead of navigating news portals. To address existing challenges and enable this transition, we introduce Semantic In-Context Learning (S-ICL), an innovative approach enabling Large Language Models (LLMs) to deliver updated news in a conversational format, enhancing user engagement and comprehension for news media