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Crime, sorrow and injustice: the highway of tears in northern British Columbia
No full textIn my thesis I explore the persistent inaction present in Canada when it comes to addressing the genocide of Missing and Murdered Indigenous Women, Girls and Two-Spirit Peoples. In this work
I focus on the need for strategic action, rather than further reports, studies and apologies from governments during this supposed time of reconciliation. Governmental inaction to advance the recommendations of the MMIWG2S+ Final Report means that: justice is not served to the victims, their families and their communities; violence against Indigenous women and girls and Two Spirit peoples is perpetuated; the trauma initiated by colonialism against Indigenous nations persists. I demonstrate how Canada's colonial history as well as the systemic violence taking place have
contributed to Canadian governments' reluctant and slow policy response to violence against MMIWG2S+ that occurs across Canada from coast to coast to coast.
Importantly, my thesis reflects my own journey of resisting the dominant Eurocentric intellectual and policy frameworks that persist in the 21st Century. As a young Indigenous woman who grew up in Fraser Lake, British Columbia, along the infamous Highway of Tears, this thesis allowed me to decolonize and reconcile my own ways of thinking, researching and writing. My analysis is informed by information and insight from the 231 Calls to Justice from the MMIWG2S+ Final Report, knowledge from Indigenous scholars, activists, and my own experience as a young Carrier woman from Nak'azdli Whut'en First Nation in British Columbia. Most importantly, I was motivated by the stories of the victims and their families whose realities with systemic discrimination, racism, and state-based violence have been reduced to statistics that are often filed and then forgotten by public officials. Each of these individuals had a family, friends, a community, a story, and a life ahead of them, but that was silenced
Understanding United States political differences in willingness to follow public health guidelines during the COVID-19 pandemic
No full textThe paper aimed to explore the relationship between COVID-19 worries (death of others vs. personal freedoms) and support for COVID-19 mitigation efforts amongst Democrats and Republicans. A total of 363 participants were included with a mean age of 40.65, 51.5% males/46.6% females, 79.1% White, 11.6% Black, and a median political orientation of 4.01(neutral). The study consisted of demographics, personality characteristics, and a between-subjects thought manipulation that prompted participants to worry about other people dying or about their personal freedoms. Following
were questionnaires concerning worries/feelings about COVID-19 and participants rated their intentions to follow COVID-19 mitigation efforts. Previous research was replicated displaying that conservatives are more interested in personal freedoms and liberals more concerned with others dying. Once personality factors were added, a more complex story unfolded. Factors such as Social Dominance Orientation (SDO) account for why Republicans are less likely to follow mitigation protocols than liberals. Other factors such as Right-Wing Authoritarianism (RWA) and Intellectual Humility (IH) displayed inverse effects to what was expected, showing that Republicans cared more about other people dying, compared to liberals. Methodological limitations and avenues for future research are discussed. Further, future research should use a control condition to allow for better assessment of the effects of political orientation and cognitive interventions
Kendall's Tau for non-independent data
No full textKendall's Tau is a non-parametric U-statistic used on bivariate data that measures whether or not these two variables are independent and, if not independent, assesses the type and degree of dependency that exists between them based on the concordance (and discordance) structure of the data. One of the assumptions of Kendall's Tau procedure requires that the paired observations be mutually independent and identically distributed (iid) according to some, known or unknown, continuous distribution. Requiring this assumption causes certain issues when exploring certain
datasets with non-iid structures, particularly, bivariate time-series data. The purpose of this thesis is to examine the behaviour of Kendall's Tau when the iid assumption is relaxed to include weakly stationary time-series data. We will discuss different methods to find a reliable variance estimate to construct accurate confidence intervals when dealing with these types of data. Additionally, we will determine which one of these estimates are best given a certain situation looking at both the advantages and disadvantages to all proposed estimates
Estimating ocean currents across the Scotian Shelf with autonomous ocean gliders
No full textAn original dead-reckoning algorithm for SeaExplorer ocean gliders was developed and used to estimate ocean currents. The algorithm was used on four glider missions that followed the Halifax Line (HL) across the Scotian Shelf, and one mission that followed the Bonavista Line (BVL) across the Newfoundland Shelf. The algorithm successfully measured the Nova Scotia Current and showed the seasonal variation from late summer to late fall. The data from the BVL was used to compare the depth measurements made by the glider's navigation sensors to the payload conductivity, temperature, and pressure sensor (CTD). The small differences in the depth measurements proved to have a significant effect on the displacement calculations in the dead-reckoning algorithm resulting in differences in both the speed and direction. The original algorithm was also compared to a dead-reckoning algorithm developed by the SeaExplorer manufacturer, Alseamar, using one of the missions from the HL. The original algorithm neglected the angle of attack of the glider, while the Alseamar algorithm parameterized it with a pitch to vx/vz ratio. To specifically analyze the effect of the angle of attack, the original algorithm was modified to include the pitch to vx/vz ratio from the Alseamar algorithm. This showed that the angle of attack was a key piece of the algorithms, as the current calculations from the original algorithm, the edited version of the original algorithm, and the Alseamar algorithm had significant differences in both speed and direction
of the currents
The Strawberry aphid, Chaetosiphon fragaefolii (Cockerell) (Hemiptera : aphididae), as a vector of strawberry decline disease associated viruses in commercial strawberries and alternative hosts
No full textIn 2012 there was an outbreak of strawberry decline disease (SDD) in Nova Scotia, resulting in crop failure. The disease was caused by Strawberry mild yellow edge virus (SMYEV) and Strawberry mottle virus (SMoV), and vectored by the aphid, Chaetosiphon fragaefolii. I nfected fields were tilled under and a provincial strawberry aphid and virusmonitoring program was implemented in Nova Scotia. New virus infections
continued to occur leading to the hypothesis that there could be inoculum outside of infected fields. Fragaria and Potentilla species are known alternative host species of C. fragaefolii, and are suspected reservoirs of viruses. A new virus, Strawberry polerovirus 1 (SPV1), was identified during the outbreak. It is hypothesized that SPV1 aids in the transmission of SMYEV and vectored by C. fragaefolii. Aphid, alternative host, and virus surveys were conducted at six field sites throughout the Annapolis Valley of Nova Scotia, and worked related to the SPV1 transmission experiment was conducted in laboratory and greenhouse settings. Aphid surveys found C. fragaefolii was the dominant colonizing species in the field, but only represented a small minority of alate aphids collected from pan traps. We determined that pan traps samples are more representative of what aphid species are present in the surrounding environment, and leaf sampling is still necessary to determine what species are colonizing a host crop. From the alternative host study, Fragaria
and Potentilla only had low numbers of C. fragaefolii, and wild Fragaria species were infected with SMYEV and SPV1, but no virus infections were found from Potentilla species. We determined that alternative hosts do not act as significant reservoirs of SDD. Implementing different production systems and shorter cropping cycles could significantly reduce the risks associated with SDD. The SPV1 transmission experiment was not completed due to continued issues with confirming virus infections in daughter plants produced from virus infected mother plants. Research still needs to be completed to experimentally demonstrate that SPV1 is transmitted by C. fragaefolii, and determine its role in the SDD complex
Vehicle traffic estimation using deep learning
No full textFor commuters, vehicular traffic is an important planning concern. People have access to the weather forecast and the current traffic situation, but there is no application available to estimate traffic congestion and flow in the near future. Similarly, traffic management authorities also seek information about future traffic for traffic management purposes. Thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data.
First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. The best models provide a Mean Absolute Percentage Error (MAPE) of 20.38% for the number of new vehicles and 18.56% for the total number of vehicles. These values are used to estimate traffic flowrate and density for hourly records over a three-month period.
The hourly traffic data is combined with observed and forecasted weather data, retrieved from the DarkSky.net website and special event data provided by the Port of Halifax to create a time series data. A Multiple Task Learning (MTL) - LSTM model is trained and tested using these data and a K-fold cross-validation approach. The Mean Absolute Error (MAE) and MAPE are used to evaluate the model performance. The MTL-LSTM model achieves a MAPE of 19.35% and 27.50% for flowrate and density using observed weather data, respectively. In the case of forecasted weather data, the MAPE for flowrate and density increases to 20.51% and 31.10%, respectively
Factors that could contribute to a collaborative gift-based leadership in the Archdiocese of Halifax-Yarmouth
No full textThe purpose of this thesis project is to explore possible factors that could characterize a collaborative gift-based leadership model between clergy and laity in the Archdiocese of Halifax-Yarmouth. This type of project allows for both laity and clergy to share their lived reality and in that sharing factors may emerge to assist the Diocese in a time of change and renewal.
Qualitative research was chosen as the most suitable method to enable hearingthe experience of both laity and clergy as they journey together as the People of God. This type of methodology allowed various voices to be heard and to share their understanding and experience in the life of a parish and in turn ministry and leadership. This research allowed for interviews with pastors, lay leaders and a parish seminar; and in these interactions, the lived reality is heard and witnessed. Possible factors that emerged from the collected data that could characterize a collaborative gift-based leadership included the need for genuine, authentic renewal at the parish level. Another factor articulated was the need for healthy relationships between clergy and laity. In order for collaboration and respect between laity and clergy, there must be trust, communication and
affirmation. Also noted was the need for transparent communication between the Archdiocese and the People of God. This type of communication is essential to keeping people engaged and connected to the wider church. The final factor is the overwhelming need for formation for both clergy and laity. Clergy need to be formed in how to be pastorally sensitive and able to journey with laity in faith. The laity need formation so as to
be affirmed and empowered to be Christ-centered leaders in their communities of faith
But, God: exploring the practice of lament as a means of developing a more secure attachment to God
No full textThe purpose of this study was to examine ways in which the practice of lament might contribute to a more secure attachment to God. Eleven participants from the London, Ontario area completed the Attachment to God Inventory (AGI) and the Experiences in Close Relationships (Revised) Questionnaire (ECR-R) before and after a seven-week study on lament and attachment. Follo wing the study, semi-structured interviews were conducted with ten of the eleven participants.
Qualitative and quantitative results indicated that the practice of lament helped to strengthen participants' perceived attachment to God. Studies of correlations between AGI and ECR-R data, which explored whether attachments between significant adult attachment relationships (ECR-R) were mirrored in some way in participants' attachment to God patterns were inconclusive, suggesting a more complex and dynamic relationship exists between believers' adult attachment relationships and the formation of attachment to God patterns
"First and foremost for our adolescents": community radio programming for teenage pregnancy prevention in Ada, Ghana
No full textThis thesis explores whether a community radio station in Ghana, called Radio Ada, represents the experiences of teenage mothers in their programing. Through participatory programming methods, social determinants of teenage pregnancy can be identified. One social factor leading to teenage pregnancy is exploitation around access to work at the Songor lagoon, which Radio Ada is a partner in advocating against.
I collected data in interviews and a focus group with teenage mothers in Ada, interviews with Radio Ada staff, and observed planning for an adolescent sexual and reproductive health and rights radio series. I evaluate
radio programming on a continuum of stereotypical to empowering (Gupta 2000). The findings demonstrate a paradox: there is capacity for Radio Ada to disrupt harmful gender and sexuality norms yet there is a disconnect between programming and the experiences of teenage mothers to whom I spoke. The methodological and theoretical frameworks are women's standpoint and postcolonial feminism
Predicting pedestrian traffic flow rate flowrate and density with deep neural networks
No full textPedestrian traffic information offers useful insights when developing or maintaining a business. This research combines image processing and machine learning methods to predict pedestrian traffic flowrate and density for up to two days into the future, based on weather data, calendar data, and special events. To obtain the traffic flowrate and density, we first developed a neural network model to predict the number of new people and the total number of people in each sequence of images captured by a Nova Scotia Webcams camera. These counts of people are used to calculate the pedestrian traffic flowrate and density labels for hourly intervals. These labels are then combined with hourly weather data, calendar data, and special event data from the same period to train a recurrent neural network to predict the traffic flowrate and density for up to two days in advance.
We try two different approaches, CNN-LSTM and dual input CNN to predict the number of new people and the total number of people from the images and compare how well each approach performs. The results show that the dual image input CNN models are more effective at predicting the number of new people and the total number of people than the CNN - LSTM models. Tested on independent test sets of images using K-fold cross-validation, theMTL CNN model achieved a test accuracy of 72% for the number of new people and 78% accuracy for the total number of people.
We trained LSTM models to predict pedestrian traffic flowrate and density using weather data, calendar data and special event data for up to two days in advance. The LSTM model has a MAPE of 33% for flowrate prediction and 33% for density prediction using observed weather data. The model also has a MAPE of 43% for flowrate and 39% for density using forecasted weather data