Carleton Undergraduate Students Present at Inquiry@Queen’s 2021

Private Lives & Unspoken Truths: How does a Right to Privacy Contribute to Violence Against Women in Pakistan?

Student Presenter: Hijaab Yahya
Faculty Supporter: Dr. Gopika Solanki

There were two core purposes of this research project. The first was to explore the impacts of the division of space within the public-private dichotomy onto women’s experiences of domestic violence (DV) and intimate partner violence (IPV) in Pakistan. In this inquiry, emphasis was placed upon understanding the impact of the culturally produced notion of the “privacy of home” on a woman’s ability to speak about and seek legal channels of assistance in her experiences of DV and IPV. The second purpose was to critically scrutinize Pakistan’s legal system in its ability to protect women from the violence they experience behind closed doors in the perceived “private” sphere of social life. By placing Art 14(1) of the Constitution of the Islamic Republic of Pakistan, which states, “[t]he dignity of man and, subject to law, the privacy of home, shall be inviolable,” as the focal point of my research project, I examined why Pakistan’s legal system legitimizes the division of public-private space by constitutionally protecting a right to privacy of home. Informed by such inquiries, I developed the question, “why does Pakistan’s legal system constitutionally entrench a right to privacy of home under Art 14(1), and how is such right balanced against a woman’s right to be free from violence?” This project was conducted virtually and remotely from Canada, which presented significant challenges, including the nine-hour time difference and efficient participant recruitment. Nonetheless, completing this research study was the most fruitful experience of my academic career. It expanded my knowledge of contemporary gender-based socioeconomic and legal issues facing Pakistan’s women and helped me to develop a network with relevant professionals and activists working in this field.

Ecosystem Carbon Fluxes Analyzed Using Eddy Covariance Technique

Student Presenter: Razz Routly
Faculty Supporter: Dr. Elyn Humphreys

Eddy covariance (EC) is an important measurement technique used in physical geography and atmospheric sciences to measure the exchange of carbon dioxide between an ecosystem and the atmosphere at a specific location. However, EC produces a net exchange of carbon dioxide yet research questions require an understanding of component fluxes, carbon dioxide uptake by plants through photosynthesis and carbon dioxide emissions due to plant and soil respiration. There are two major methods to partition EC measurements into these component fluxes: night-time and day-time partitioning methods. In the night-time method, nighttime measurements are used to estimate daytime respiration and calculate photosynthesis as a residual and in the daytime method, a light response curve is created to estimate daytime respiration and photosynthesis. This study investigates the benefits and drawbacks of these partitioning methods on two carbon dioxide exchange datasets from ecosystems in Canada.

Supporting Student Visualization of Molecular Processes Through Diagram Drawing

Student Presenter: Tara Thachet

Faculty Supporter: Dr. Martha Mullally

Historically, scientists and researchers have accompanied their observations with drawings, indicating that visual models are an effective way of communicating science. Studies show that students should draw images that are either interpretational or transformative, regardless of artistic ability, as they help to improve learning. However, many educators do not utilize this practice in their courses. In this study, we investigated if making simple and schematic drawings can help students understand complex molecular processes, and to contextualize complex plant biology processes in an undergraduate plant biology course. When students were introduced to a complex plant process, the instructor accompanied their explanation with a simple schematic drawing. Students were told by the instructor that 1-2 drawing questions would appear on the midterm. For the final exam, no questions explicitly asked students to include a schematic drawing. Students who drew often scored higher on questions related to the topics where drawings were introduced in the course and the lab. Students who drew on the final exam did 12.3% better on the exam than those who didn’t draw. Students who had continuous exposure to drawing style questions during the midterms, did 6% better in the course compared to students who did not write the midterms. Students also gave an overwhelmingly positive response to drawing, and 94% of the surveyors believed that making simple drawings helped them to learn complex molecular processes. This could indicate that exposure to drawing style questions helped reinforce the learning of these processes.

The Line Between Politics and Conspiracy Theories: Tracking Disinformation using #StopTheGreatReset

Student Presenter: Ashli Au

Faculty Supporter: Dr. Michael Christensen

Have you heard? In today’s pandemic, the Trudeau administration has been using the widespread lockdowns to impose socialism in Canada. This conspiracy theory has been mobilized under the hash tags #StopTheGreatReset, #Scamdemic and #CancelTheLockdown amongst others. With the COVID-19 pandemic, as with previous major events, there has been an influx of dis-and mis- information on social media platforms. This rapid spread of information can have strong influences on people’s behaviour which can impact the effectiveness of public health measures taken by governments (Cinelli et al. 2020; González-Padilla and Tortolero-Blanco 2020). My research is part of an ongoing project that aims to identify and map the spread of disinformation, and its effects on Canadian society. For this sub-project, I created a database of social media posts from Twitter accounts that promote or spread disinformation narratives directed towards Canadian politics and public health measures. From this, we were able to identify some of the most common narratives of disinformation in circulation on Twitter; the hash tag #StopTheGreatReset was chosen as the focus of the project to study the fine, and often blurred, line between legitimate politics and conspiracy theories. Going forth, my aim is to conduct a qualitative analysis on the links attached to social media posts fueling disinformation to understand what kinds of information are being circulated and identify common themes. This project has been an opportunity for me to learn about how social media research is conducted and allows me to engage with urgent issues in contemporary media culture.

Changes in Knowledge Across Time: What do Children Know About What They Know?

Student Presenters: Della Boudreau & Lily Toutounji

Contributors: Kenda Parsons, Vivian Rigg, Ellen Doucet, Lojain Hamwi & Deepthi Kamawar

Our study focuses on children’s understanding of their own knowledge and how it changes over time. Preschool-aged children perform above chance when asked about current knowledge, but only children older than 5 years of age performed above chance for past, future, or intraindividual knowledge (Atance & Caza, 2018; Caza et al., 2016). However, we do not currently know whether awareness of past and future knowledge is related. While this type of awareness seems conceptually related to metacognition (the awareness of one’s own ignorance or knowledge; Rohwer et al., 2012), the relation to this skill is unknown. Thus, the goal of the current study is to investigate how children’s awareness of their own epistemic knowledge is related to their metacognitive abilities. This study will explore children between the ages of 3.5- through 5-years-old, who will be assessed on their understanding of their current, past, and future knowledge, as well as other tasks assessing metacognitive skills. Further, we will explore the role of theory of mind and inhibitory control. We predict that children who do well on the epistemic knowledge task for the past will display better performance on the task asking about the future, and that both will be related to the other cognitive skills measured. Due to the current global situation, we converted our study materials to an online format. Our poster will highlight this process and discuss ways to approach challenges in online developmental testing. Though data collection is ongoing, we present initial insight into the process, drawbacks, and benefits of online testing.

Keywords: Epistemic Knowledge, Metacognition, Theory of Mind, Child Development

Automated Emotion Classification in Free-Moving Rats: Exploring a machine learning pipeline to improve emotion-data in animal models

Student Presenter: Andre Telfer
Contributors: Frances Sherratt, Oliver van Kaick & Alfonso Abizaid

Studies involving emotion often use animal models and currently rely on manual labelling by researchers. This human-driven labelling approach leads to a number of challenges such as: long analysis times, imprecise results, observer drift, and varying correlation between observers. These problems impact reproducibility, and have contributed to our lack of understanding of fundamental mechanical questions such as how emotions arise from neuronal circuits. Recent success of machine learning models across similar problems show that it can help to mitigate these challenges while meeting or exceeding human accuracy. We developed a classifier pipeline that takes in videos and produces an emotion label. The pipeline extracts body part positions from each frame using a pose estimator and feeds them into an Artificial Neural Network (ANN) classifier built using stacked Long Short Term Memory (LSTM) layers. The data was collected by treating nine rats with Lypopolysaccharide (LPS) injections (10mg/kg). First, rats were recorded for 10 minutes under control conditions with no manipulation and no observed symptoms of stress or malaise. A week later, rats were injected with LPS and filmed for 10 minutes two hours post-injection. The classifier pipeline developed correctly labelled 78% of the 125,040 video segments from 8 test videos. When combined with a vote-based system, this led to 7 of the 8 test videos being classified correctly which was the same accuracy attained by a human expert from the lab. The test videos had varying environments and used rats that were different from the training videos, providing evidence of a degree of robustness in the model. Future work will focus on expanding the test data and incorporating models for 3D pose estimation and behavioral classification.