ETD SWORD Deposit

Permanent URI for this collection

https://hdl.handle.net/10315/27543

ETDs in this collection are being checked for completeness and are in the process of being transferred to their respective collections under the FGS ETD collection umbrella.

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Open Access
Exploring Opportunities to Enhance Engagement in Home-Based Stroke Rehabilitation Through the Design of Instrumented Objects
(2025-04-10) Wong, Joey Shon Yue; Desai, Shital
This thesis explores opportunities to enhance patient engagement in stroke rehabilitation through the design of an instrumented object aimed at improving fine motor skills at home. Using a Research through Design approach, the study focuses on the development of MorlensRehab, a sensor-based device that incorporates occupational therapy exercises. The prototype was used in a co-creation workshop to identify and better understand the needs and experiences of a diverse group of stakeholders, including rehabilitation patients, caregivers, healthcare providers, and technology developers. A thematic analysis of the workshop data uncovered key themes such as motivation and engagement, monitoring health and wellness, and social connectedness, highlighting factors that influence the adoption of technology in rehabilitation. Insights from the workshop also provided recommendations for more user- friendly iterations of MorlensRehab and emphasized the importance of developing an ecosystem of supporting products that can enhance patient engagement and the adoption of rehabilitative technologies.
Open Access
Lay Beliefs About Sexual Satisfaction And Attributions For Low Desire In Relationships
(2025-04-10) Stephanie Raposo; Amy Muise
Sexuality plays a key role in shaping overall relationship happiness and stability, yet sexual desire tends to decline over time in relationships, which is a key reason for relationship dissolution. Theories of implicit (lay) beliefs about the maintenance of sexual satisfaction (i.e., sexual growth and sexual destiny beliefs) provide a valuable framework for understanding how people cope with sexual challenges, such as low desire, in a relationship. My dissertation extended theories of lay sexual beliefs by exploring associations with sexual and relationship well-being among people responding to both hypothetical and lived experiences of clinical low desire. I also tested novel mechanisms for these effects—the attributions that people make for the cause of their low desire. In Study 1, a study of individuals in relationships, sexual beliefs were associated with well-being, which was, in part, accounted for by the attributions people assigned to hypothetical low desire and arousal. In Study 2, sexual beliefs were differentially associated with relationship and sexual well-being in couples coping with clinically low desire. I found similar results in Study 3, a daily diary study of couples coping with clinically low desire, which were again, partially explained by daily attributions. Lastly, in Study 4, an experimental study, people oriented toward sexual growth (versus destiny) reported higher control attributions, which in turn was associated with people finding the situation less challenging. My dissertation demonstrated the critical and nuanced role of attributions in understanding how sexual beliefs are associated with relationship and sexual well-being in response to low desire.
Open Access
Design, Development, and Validation of an End-User Photo-Thermal Sensing Platform for Rapid Detection and Quantification of Analytes in Fluidic Samples
(2025-04-10) Hayden, Derek William; Nima Tabatabaei
The ability to detect the presence of specific analytes and quantify their titers in fluidic samples is essential in many industries, spanning from food industries to law enforcement to healthcare and beyond. The existing technologies used for this purpose require the use of specialty equipment by trained professionals in a laboratory setting to function (e.g., mass spectrometry or ELISA) which greatly increases the cost and time taken to receive actionable results. Portable and inexpensive tests exist – Lateral Flow Immunoassays – however these tests are only qualitative and frequently have an inferior limit of detection. To date, several sensing devices have been designed to interrogate these LFIAs and decrease their limit of detection, however, these devices are often prohibitively expensive. This thesis outlines attempts to design and validate a sensing platform which could inexpensively enhance the limit of detection of LFIAs.The prototype is then validated through both lab-based and human experiments.
Open Access
A Speech Analysis Comparison of People with Parkinson's Disease and Healthy Controls
(2025-04-10) Karimi, Ashkan; Joseph FX DeSouza
This study explores the effects of Parkinson’s Disease (PD) and a dance intervention on vocal features—specifically fundamental frequency standard deviation (F0SD) and intensity standard deviation (IntSD)—in individuals with PD and healthy controls over five years. The findings suggest that while both groups exhibited changes in these vocal features over time, the differences between the PD and control groups were moderate and likely masked by individual variability. F0SD, in particular, showed distinct patterns of change over time between the two groups, aligning with known voice impairments in PD. However, changes in IntSD appeared to be more influenced by the normal aging process rather than PD itself. Despite the lack of significant post-dance improvements, the study highlights the potential value of speech features as biomarkers for PD, emphasizing the need for larger, more intensive studies to fully understand the effects of interventions like dance on vocal performance in PD.
Open Access
Micro and Nano-structured Materials with Controlled Radiative Properties for Radiative Cooling Applications
(2025-04-10) Pirvaram, Atousa; Paul G. O'Brien
In response to global environmental crises, such as climate change, urban heat islands, and escalating energy demands, this thesis investigates innovative solutions to these challenges through the development of radiative cooling (RC) materials and systems. The research first examines the potential of RC technologies to mitigate climate change by assessing their impact on global warming potential (GWP) and radiative forcing (RF) through life cycle assessment (LCA) methods. The study analyzes RC materials in comparison with conventional construction and roofing structures, highlighting their significant potential to mitigate global warming impacts. A RC material exhibiting an average solar reflectance (R ̅_solar) of 98.2% and an average long-wavelength infrared emittance of (ε ̅_LWIR) 98.5%, achieved a net cooling power of 160.8 W·m⁻², leading to a GWP of -252 kgCO₂-eq·m⁻² over 20 years and -333 kgCO₂-eq·m⁻² over 100 years, with an RF value of -1.01 W·m⁻² when covering 1% of the Earth's surface, indicating a substantial reduction in radiative forcing compared to conventional materials. The thesis then explores the incorporation of underside reflective surfaces to boost RC performance by redirecting thermal emissions toward the sky and reducing heat loss. Numerical simulations using Monte Carlo ray-tracing (MCRT) techniques were employed to evaluate the cooling effectiveness of various configurations, including flat and parabolic reflectors. Results show that considering an ideal selective emittance spectrum, in the absence of solar absorption and convective heat transfer, at an ambient temperature of 300 K, the steady-state temperatures using parabolic reflectors with the mentioned geometrical features can be cooled down to approximately 230 K. An important objective was to design and analyze novel configurations of micro- and nano-structured materials with controlled radiative properties for passive daytime radiative cooling (PDRC) applications. Key advancements included the development of enhanced PVDF-HFP-based porous materials with high solar reflectivity and long-wave infrared emissivity, such as the aforementioned (1-8-1.25) sample, fabricated via the phase inversion method. These materials were designed to maximize cooling by minimizing solar heat absorption and enhancing thermal radiation through the atmospheric window. Finally, experimental validation was conducted via outdoor testing and controlled dew condensation experiments in a dedicated setup, demonstrating the practical applicability of the developed PDRC systems. The findings underscore the significant potential of RC technologies to reduce cooling energy demands and mitigate global warming, making them a promising approach to enhance sustainability in urban and building environments.
Open Access
In the Circle of Fire: Gendered Barriers in Fire Services in Ontario
(2025-04-10) Rizzi, Deryn Nicole; Meg Luxton
The firefighting profession is described as inherently dangerous, rich in pride, honour and tradition. Firefighters are held in high regard, as they are known for their involvement in, and commitment to the community. Firefighting is a ‘public safety’ service, with a labour force that is predominantly white males. The public expect firefighters to fight fires and rescue those in distress, displaying heroism, strength and embodying masculinity (Yarnal et al., 2016). Although described as a masculine profession, the role of the firefighter is changing, and the composition of the service is beginning to evolve to reflect the community that it serves. This phenomenological study, guided by the principles of standpoint theory, investigates gender-based workplace dynamics within firefighting, uncovering ways in which nuanced stereotypes, bias and discriminatory practices contribute to a less inclusive and sometimes unsupportive environment for women in the Fire Services in Ontario. Thirty-two firefighters participated in semi-structured interviews. The themes presented are generalized to both genders, as well as themes unique to either male or female firefighters. This study’s findings reveal that while some themes are found to apply to both genders, others are distinct to women firefighters. This dissertation highlights the negative impacts the workplace has on women firefighters.
Open Access
Deciphering Ion Channel Dynamics: A Clustering Approach for Signal Analysis
(2025-04-10) Kazemi, Mohammadreza; Andrew W. Eckford
This thesis investigates clustering techniques for analyzing multi-channel CFTR ion channel activity recorded via the patch-clamp method. Accurately determining the number of active channels and classifying their states is crucial for understanding ion channel dynamics. Both classical and machine learning approaches are explored. Classical methods, including DBSCAN and a hybrid DBSCAN-BGMM approach, show limitations in handling noise and overlapping states, particularly with increasing channel counts. A novel machine learning approach combining a Cluster Count Neural Network (NN) for channel number estimation and a Long Short-Term Memory (LSTM) network for state classification demonstrates superior performance. The NN effectively captures underlying patterns while the LSTM leverages temporal dependencies, achieving higher accuracy and robustness even with complex, noisy signals. This research offers promising tools for analyzing ion channel activity and has implications for cystic fibrosis research and drug development.
Open Access
Application of GNSS Precise Point Positioning to Low-Cost Hardware for cm-level Positioning
(2025-04-10) Basnet, Pragati; Sunil B Bisnath
Precise Point Positioning (PPP) offers high-precision GNSS positioning solutions. The advent of low-cost hardware provides an affordable alternative to costly geodetic-grade hardware, broadening the accessibility of high-precision positioning across many applications. However, this hardware produces measurements with higher noise levels, reduced multipath suppression, and lower carrier-to-noise density ratios (C/N0), restricting its ability to achieve cm-level accuracy. This study addresses these limitations by developing a novel C/N0-based empirical observation weighting model to accompany the signal characteristics of low-cost hardware. This model enhances positioning accuracy by emphasizing high-quality signals above a nominal C/N0 threshold and down-weighting observations below it. The proposed model reduces float to carrier-phase integer ambiguity resolution (fixed) convergence time by 71% for 5 cm and 38% for 2.5 cm horizontal error thresholds for the static dataset tested, demonstrating the potential of low-cost GNSS devices as viable, high-precision positioning solutions.
Open Access
Integrated Analog Readout Array and Digital Backend for Mobile DNA Sequencing
(2025-04-10) Dawji, Yunus Ibrahim; Sebastian Magierowski
DNA, a fundamental biomolecule, contains the genetic code that governs the development, functioning, and reproduction of all living organisms. It is composed of smaller molecular units called nucleotides. The process of determining the specific sequence of these nucleotides is known as DNA sequencing. An innovative approach to this is nanopore-based DNA sequencing. Unlike many other methods, nanopore sequencing detects DNA molecules directly, rather than relying on secondary phenomena, and can do so in real-time as the molecules pass through the device. This technology holds the potential to significantly democratize DNA sequencing, which could revolutionize medical diagnostics and personalized medicine, ultimately improving the lives of billions. This report focuses on optimizing the performance and cost-efficiency of nanopore-based sequencing, particularly by exploring the opportunities for implementing low-cost, integrated analog front-end arrays and application-specific accelerated digital back-end systems. This thesis presents three iterative versions of a digital readout integrated circuit (DROIC), each enhancing throughput density through architectural and circuit-level advancements. The first version (DROICv1) employs a discrete-time (DT) amplifier with in-pixel successive approximation ADCs. The second version (DROICv2) increases throughput density using column-based ADCs. The third version incorporates an asynchronous reset amplifier, further enhancing throughput density by reducing amplifier noise. To validate the system's functionality, the thesis demonstrates biological ion-channel and solid-state nanopore measurements. It also introduces methods for post-processing the chip to enable on-chip sensors. Finally, a RISC-V-based digital basecaller is presented, optimizing the speed and energy efficiency of the digital backend.
Open Access
Title of dissertation From Discrete to Continuous: Learning 3D Geometry from Unstructured Points by Random Continuous Space Queries
(2025-04-10) Jia, Meng; Matthew J Kyan
In this dissertation, we focus on generalizing recent point convolution methods and building well-behaved point-cloud 3D shape features to achieve more robust, invariant, and versatile implicit neural representations (INR) of 3D shapes. In recent efforts to explore point-cloud based learning methods to improve 3D shape analysis, there has been much attention paid to the use of INR-based frameworks. Existing methods, however, mostly formulate models with an encoder-decoder architecture that incorporates a global shape embedding space, which often fails to model fine-grained local details efficiently, limiting overall generalization performance. To overcome this problem, we propose a convolutional feature space sampling operation (Dual-Feature Sampling or DFS) and develop a novel INR learning framework (Stochastic Continuous Function Learning or SCFL). This framework is first adapted and evaluated for its use in surface reconstruction of generic objects from sparsely sampled point clouds, which is a task that has been extensively used to bench-mark INR 3D shape learning methods. This study demonstrates impressive capabilities of our method, namely: 1) an ability to faithfully recover fine details and uncommon shape characteristics; 2) improved robustness to point-cloud rotation; 3) flexibility to handle different levels of sparsity in the input point clouds; 4) significantly better generalization in the presence of unseen shape categories. In addition, the proposed DFS operator proposed for this framework is well-formulated and general enough that it can be easily made compatible for integration into existing systems designed to address more complex 3D shape tasks. In this work, we harness this powerful ability to represent shape, within a newly proposed SCFL-based occupancy network, applied to shape based processing problems in medical image registration and segmentation. Specifically, our network is adapted and applied to two different, traditionally challenging problems: 1) liver image-to-physical registration; and 2) tumour-bearing whole brain segmentation. In both of these tasks, significant deformation can severely degrade and hinder performance. We illustrate however, that accuracy in both tasks can be considerably improved over baseline methods using our proposed network. Finally, through the course of the investigations conducted, an intensive effort has been made throughout the dissertation to review, analyze and offer speculative insights into the features of these proposed innovations, their role in the configurations presented, as well as possible utility in other scenarios and configurations that may warrant future investigation. It is our hope that the work in this dissertation may help to spark new ideas to advance the state of the art in learning-based representation of 3D shapes and encourage more interest in novel applications of INR to solve real-world problems.
Open Access
Love as a Remedy to the Malaise of the Soul in Modernity
(2025-04-10) Berezowski, Madelyn Helena; Mario Di Paolantonio
The purpose of this thesis project is to both diagnose the current malaise of the soul, and offer a potential remedy to this malaise. By attending to Hannah Arendt’s notion of “worldlessness” drawn from her book The Human Condition, I will argue that the condition of worldlessness and subsequent feelings of loneliness result in a “malaise of the soul”. The remedy to this malaise may be found, I will argue, in the proper type of love. When viewed within the canon of literature on philosophy of education, this project addresses a significant lack of philosophical depth when considering love as central to education and pedagogies of love. While numerous scholars have argued for the importance of love in education, these works fall short in offering a complete philosophical understanding of love itself. This project draws on ancient philosophy, specifically Socrates’ arguments as presented by Plato in Phaedrus and Symposium, to address this lacuna. By bringing a robust understanding of love to Arendt’s work, it aims to offer love as a remedy to the current malaise of the soul.
Open Access
Resident Space Object Light Curve Classification & Space Situational Awareness Sensitivity and Simulation Studies
(2025-04-10) Qashoa, Randa; Regina S. K. Lee
The number of objects being launched into space is rapidly increasing, emphasizing the critical importance of detecting, characterizing, and tracking these objects—an area of focus known as Space Situational Awareness (SSA). These Resident Space Objects (RSOs) include satellites (both active and inactive), rocket bodies and debris. Knowing the type of object near our satellites of interest is very important as it gives satellite operators the knowledge needed to accurately plan maneuvers to keep our orbits safe. This dissertation explores three main contributions within the field of SSA. The first is a light curve classifier which uses Machine Learning (ML) to classify Low Earth Orbit (LEO) RSOs into stable satellites, tumbling satellites and rocket bodies. Multiple approaches were tested but the method with the highest accuracy is a Barlow Twins network which has a 75% accuracy for two minute light curves and a 97% accuracy for five minute light curves. The classification is used to characterize the motion of objects, which operators can use in combination with real images to determine the risk of collision and to perform effective maneuvers. The second contribution is regarding SSA mission planning. A sensitivity analysis was conducted to determine the best camera to use for observing co-orbiting RSOs within 250 km of the observer. The analysis includes exploring the location of potential targets in the Field-Of-View (FOV) of the observer as well as the Signal-to-Noise Ratio (SNR) of different targets. A similar analysis to the one presented in this dissertation has been performed for the Redwing microsatellite mission. Lastly, RSO image prediction simulations are tested for use in SSA. This dissertation demonstrated the implementation of an anti-sun pointing orientation for prediction simulations with validation from real images. Predicted images were used to determine targets for observation which were then validated following the downlink of the images.
Open Access
Automatic Instantiation of Assurance Cases from Patterns using Large Language Models
(2025-04-10) Odu, Oluwafemi John; Alvine Boaye Belle
Justifying the correct implementation of mission-critical systems' non-functional requirements (e.g., safety, and security) is crucial to prevent system failure. The latter could have severe consequences such as the death of people and financial losses. Assurance cases can be used to prevent system failure. They are structured sets of arguments supported by evidence, demonstrating that a system’s non-functional requirements have been correctly implemented. Assurance case patterns serve as templates derived from previous successful assurance cases, aimed at facilitating the creation of new assurance cases. Despite the use of these patterns to generate assurance cases, their instantiation remains a largely manual and error-prone process that heavily relies on domain expertise. Thus, exploring techniques to support their automatic instantiation becomes crucial. To address this, our thesis explores the literature on assurance case patterns to understand recent advancements and trends characterizing that literature. Then we investigated the potential of Large Language Models (LLMs) in automating the generation of assurance cases that comply with specific assurance case patterns. Our findings suggest that LLMs can generate assurance cases that comply with the given patterns. However, this study also highlights that LLMs may struggle with understanding some nuances related to pattern-specific relationships. While LLMs exhibit potential in the automatic generation of assurance cases, their capabilities still fall short compared to human experts. Therefore, a semi-automatic approach to instantiating assurance cases may be more advisable at this time.
Open Access
Development of Adaptive Tracking Methods with Enhanced Performance Based on Deep Learning
(2025-04-10) Zhang, Shuo; Dan Zhang
Adaptive object tracking aspires to locate the target incessantly in each frame with designated initial target location, which is an imperative yet demanding task in computer vision. Recent adaptive approaches strive to fuse global information of template and search region for achieving promising tracking performance. However, fusion of global information devastates some local details. Local information is essential for distinguishing the target from background regions. To address this problem, we present a novel tracker TGLC integrating a channel-aware convolution block and Transformer attention for global and local representation aggregation, and for channel information modeling. Experimental results demonstrate the superior tracking performance of TGLC. Ablation experiments further verify the effectiveness of multiple information aggregation for improving tracking performance. Long-term tracking is a vital component in real-world tracking scenarios. Recently, one-stage long-term trackers achieve state-of-the-art tracking results due to more sufficient integration of search and template representations. These methods usually adopt an encoder for synchronous feature generation and interaction. Despite their high performance, the approaches tend to feed the encoder full input representations that are highly redundant during training. A novel algorithm MIMTracking is developed for tackling this problem. MIMTracking exploits an encoder and a decoder for masked image modeling during training. This design alleviates input redundancy and reduces the computational cost of the training process. The proposed MIMTracking achieves state-of-the-art tracking results on numerous datasets. Addressing tracking challenges is an essential topic in real-world applications. Constantly varying appearance of targets brings tremendous challenges for object tracking, especially in background clutter scenarios. Current leading trackers attempt to introduce dynamic templates to encode changing target information. However, dynamic templates are obtained from intermediate frames that are not manually annotated. Therefore, these templates may contain a large amount of uninformative and irrelevant background noise due to imprecise tracking. To tackle the problem, a novel tracker ATPTrack is proposed for tracking. Particularly, ATPTrack develops an alternating token trimming method that prunes dynamic templates and search region progressively. Compared to merely trimming the search region, ATPTrack further reduces MACs by 11.5% with negligible performance drop of 0.3% by alternately pruning dynamic templates and search region.
Open Access
Intruders' Behavior Unveiled: A Dual-Tier Behavior-driven Model for Malicious Activity Detection in IoT Network Using Graph Learning
(2025-04-10) Shafi, MohammadMoein; Arash Habibi Lashkari
In recent years, IoT technology has transformed smart homes, with most households now including several IoT devices that provide convenience and automation. However, the security of these smart homes is paramount, as vulnerabilities can expose residents to risks like unauthorized access, data breaches, and operational disruption. Network-based threats pose a particularly critical risk due to the numerous vulnerabilities in wireless communication between devices, making it possible for attackers to intercept data or do malicious activities. While traditional intrusion detection systems exist, they are often ineffective in detecting zero-day attacks and lack the ability to identify malicious patterns across diverse threat scenarios due to limited diversity in their detection models. Moreover, these systems are not designed to fully detect all types of intrusions, especially those involving both external network activities and internal IoT communications among smart home devices. This gap is made worse by the challenges in creating specialized IoT datasets that cover a diverse set of malicious activities and data types, which require extensive technical knowledge, a diverse range of devices, and expertise in capturing, executing, and labeling attack scenarios. Such datasets are crucial for data-driven intrusion detection systems. Addressing these challenges, this thesis introduces a dual-tier detection system that effectively can zero-day attacks, and is designed in a way to be scalable for learning the behavior of diverse malicious activities. the proposed solution leverages data from both the smart home hub’s internet connection and the internal network communication of IoT devices to detect and profile malicious activities using a novel graph learning approach. Furthermore, to support this research, we have created the largest IoT smart home dataset, incorporating real-world data from over 50 devices and more than 100 carefully designed attack scenarios, captured over a five-month period. The analysis of this dataset and the performance of our detection model demonstrate promising results, providing a valuable resource and foundation for advancing smart home IoT security.
Open Access
Exllu(gesis)
(2025-04-10) Ancheta, John Robert; Marc G Couroux
Exllu (2024) is a text-and-image-based digital artwork that presents as a literary publication of paranoid fiction. Set within the ubiquitous surround of U-City, Exllu plots a game-like condition that perpetually breaks down and reconstitutes itself through the quotidian effects of its intimate and remote contests, inciting far-flung and jarring ideas concerning what this strangely familiar scenario affords and obscures, promises and threatens. In Exllu's exegetical companion text, "Exllu(gesis)" (2024), I relaunch and perform the artwork's pretence of paranoid fiction while expounding on its underlying theme of gamespace and dissimulative treatment. I evince Exllu as a systems thinking and, equally, an analogical encounter with the ubiquitous, control-oriented technologies of the contemporary milieu, and a generative endeavour of unsettling dominant scopes of world-making.
Open Access
IMAGE PROCESSING FOR STRATOSPHERIC BASED SPACE SITUATIONAL AWARENESS (SSA)
(2025-04-10) Suthakar, Vithurshan; Regina S.K. Lee
This research explores the use of a stratospheric platform imager for advancing Space Situational Awareness (SSA). The primary goal was to develop and validate Resident Space Objects (RSO) detection algorithms using the RSONAR dataset, consisting of wide field-of-view imagery. RSO Detection methods were tested on 429 images, achieving F1 scores between 68% and 88%. Additionally, the potential of a dual-purpose star tracker for SSA was validated, analyzing over 27,000 images to assess astrometric and photometric properties of RSOs. Further, 544 RSO streaks were characterized based on parameters such as length, signal-to-noise ratio, and orientation. The development of RSONAR II, a next-generation camera system, allowed for capturing over 65,000 images at varying resolutions, and its optical performance was compared across two imaging systems. This study provides a comprehensive evaluation of wide field-of-view imagery for SSA and presents advancements in dual-purpose star tracker systems for future missions.
Open Access
Transmission Dynamics And Control Of Cholera In Africa: A Mathematical Modelling Approach
(2025-04-10) Adeniyi, Ebenezer Olayinka; Jude Kong
Background: Cholera, caused by Vibrio cholerae, is a global health threat, with outbreaks surging since 2021, particularly in Africa. In 2024, over 13 African countries faced outbreaks worsened by climatic events, poverty, and weak healthcare systems. A shortage of vaccines further complicates control efforts. Objective: This study uses data science, machine learning, and modelling to analyze cholera dynamics, identify outbreak drivers, and propose targeted interventions. Methods: A compartmental model with Bayesian estimation analyzed cholera data from eight African countries. Sensitivity analysis identified key transmission parameters, and hierarchical clustering grouped countries by outbreak characteristics. Results: Average R0 was 2.0, ranging from 1.41 (Zimbabwe) to 2.80 (Mozambique). Factors like infection rate and human shedding increased R0, while recovery rate reduced it. Clustering identified three outbreak drivers: natural disasters, conflict, and sanitation issues. Conclusion: Tailored, data-driven interventions are critical for effective cholera management across diverse contexts.
Open Access
Invisible Barriers Gendered Problems in Canadian Law Faculties 1961 to 1994
(2025-04-10) Starr, Taylor Demi; Marlene Shore
This dissertation examines the history of the entry of women into the Canadian legal academy, focusing on the period from 1961 to 1994. The 1960s marked the gradual appearance of women in law faculty positions, with the study concluding in 1994, the year following the publication of The Canadian Bar Association’s Touchstones for Change Report. Despite highlighting successes, this report only briefly delved into the experiences of women law professors and the discrimination against them that persisted beyond 1993. Despite an increase in female law students from the mid-1970s, women remained significantly underrepresented as faculty members, facing invisible barriers that hindered their progress. Titled “Invisible Barriers,” the study explores various facets of women’s experiences in the academy, including women’s educational backgrounds, course assignments, administrative practices, and their contributions to legal literature, particularly feminist legal theory. Examining all twenty-one Canadian law faculties in this time frame, the research unveils unique patterns within civil and common law institutions, with Québec law faculties leading the way in incorporating women into the long inscribed androcentric sphere of law faculties. The study delves into the numerical growth of women faculty, revealing intriguing statistics and experiences that challenge conventional expectations. It also investigates the political, cultural, and socio-economic conditions that influenced women’s experiences in the legal academy. The dissertation contextualizes the struggles of women in the legal academy within the broader history of Canadian legal education. It examines how women’s access to higher education has evolved, tracing the shift from segregated conditions in the nineteenth century to coeducation. The study highlights key figures who challenged societal norms, including the first women to earn degrees in law and their subsequent achievements. It provides a new way in which to interpret women’s participation in male-dominated spheres, emphasizing the importance of understanding the experiences of those who were marginalized and often overlooked. The dissertation offers a different perspective by examining the legal academy’s role instead of focusing solely on practicing lawyers or women in the judiciary. It provides a thorough analysis of the systemic barriers women encountered and of the gradual changes within the Canadian legal academy.
Open Access
The Creatures of the Province Doctrine and the Neoliberalization and De-Democratization of Local Governance in Toronto from 1996-2023
(2025-04-10) Kelpin, Ryan Justin; Karen Murray
In 2022, Ontario Premier Doug Ford announced new legislation, the Strong Mayors, Building Homes Act. The Act enshrined something once unthinkable to people living in the City of Toronto: granting the mayor the power to veto decisions made by the City Council while requiring the council to summon a super-majority (two-thirds) of votes to overrule the mayor. Any new local bylaw passed seen as clashing with “provincial priorities” could be vetoed by the mayor, creating a direct political link on all local issues between the municipal mayor’s and provincial government premier’s offices. This dissertation questions broad assumptions about, and examines and theorizes to what degree, this type of governing was a stark break from, or a continuation of, the longstanding norms pertaining to liberal democratic institutions and intergovernmental governance between the Government of Ontario and the City of Toronto. Using three case studies over the period from 1996 to 2023, the dissertation focuses on three Ontario premiers: Mike Harris (1996-2002), Dalton McGuinty (2003-2006), and Doug Ford (2018-2023). The argument put forth is that in the neoliberal era, the provincial government has routinely utilized the creatures of the province doctrine to restructure liberal democratic institutions and undercut democratic decision-making processes within the City of Toronto. This was done in the name of the neoliberalized notion of “efficiency” through centralizing power and insulating neoliberal austerity measures from critique.