Are Bystanders More Persuasive Than Victims? The Impact of Social Media Backlash Toward Brand Transgressions.

In the digital age, brands face increasing scrutiny for their perceived transgressions. Influencers and social media commentators play pivotal roles in raising awareness and mobilizing collective action against such brands. Our research question delves into the comparative persuasiveness of bystanders versus victims in social media backlash toward brand transgressions: “Are bystanders more persuasive than victims? The impact of social media backlash toward brand transgressions.” We collected data through interviews and archival research to inform a study on consumer perceptions of brands and their marketing materials, particularly focusing on diversity efforts. Our goal is to gain insights into how consumers perceive brand diversity initiatives.

We conducted interviews with a diverse group of participants, varying in age, gender, interests, and knowledge on the subject. Participants were asked a series of questions regarding their perceptions of brand diversity efforts. Additionally, they engaged in a projective exercise where they designed an inclusive advertisement for a fictional brand in a category they felt connected to. These interviews, alongside relevant literature, underwent coding and analysis.

Our findings suggest that social media users are directly affected by potentially unethical marketing practices highlighted by influencers; and they form attitudes towards the social issues based on how relatable they found the influencer. This research holds significance as it could enhance societal understanding of the impact of social media backlash on marketing activities. Furthermore, it aims to ascertain whether the source of the backlash and how it’s communicated further shapes consumer perceptions of brands and their marketing endeavors.

Continue reading Teagan Hawes »

“Can you make coffee wrong, anyway?” : An Ethnographic Analysis of the Coffee Culture in Lewisburg, PA

The “three waves” of coffee describe the growing importance of unique flavors and sourcing in order to best satisfy an increasingly sophisticated palate in coffee consumption. These allude to people’s preferences for different kinds of coffee: third wave roasters emphasize the importance of traceability with their coffee beans, which in turn adds a level of education that is often referred to as the “coffee geek subculture”. Conversely, the first wave does not rely on quality in order to sell, but rather on coffee as a mass product that delivers caffeine to its consumers. The second wave, then, intersects these two extremes, and relies on chain coffee houses to create more individualized coffee drinks, though they do not focus on value orientation of the product as heavily as third wave enthusiasts. My project examines the manifestation of these three waves in Lewisburg’s coffee scene. I conducted interviews with both producers and consumers who live in our town to learn about whether or not their own coffee consumption intersects the three waves. Through this, I discovered a similar value-oriented system that distinguishes coffee consumers from one another: in Lewisburg, a person’s consumption habits are driven by either a social, ethical, or economic value orientation. In this thesis, I analyze the three wave typology to challenge current understandings of coffee culture in the United States.

Continue reading Elizabeth Hoffman »

Building Large Language Models

ChatGPT became available in November 2022. It is the most widely used large language model. This research explores uses of ChatGPT in a variety of industries. We also explore building our own large language model using nanoGPT. NanoGPT offers the opportunity to create a small scale large language model on a single computer. Research questions are ‘How are industries using ChatGPT?’ and ‘How can we create our own large language model outside of ChatGPT?’

Continue reading Ben Khant »

Exploring Variations in Strength and Mechanical Properties of Lumbar Vertebrae in Eulipotyphla

This study examines potential strength variations among lumbar vertebral units, driven by functional trade-offs. Microindentation testing is employed to elucidate mechanical property discrepancies across distinct vertebral units. Hypothetically, shrew lumbar vertebrae features will be correlated with mechanical attributes, including stiffness and load-bearing capacity. We are predicting that the vertebrae may show adaptations in strength with a trend of increasing strength as one descends the vertebral column. This could be driven by the need to balance the mechanical demands of supporting weight and providing stability with the requirement for flexibility and mobility in the lower lumbar region. In other words, the transition from weight-bearing to facilitating movement prompts a distribution of strength to address these functional trade-offs, consequently yielding the observed pattern.

Continue reading Myleen Amendano »

The American Dream: Divergence between Class, Race, and Gender

The term “American Dream” encompasses diverse notions of economic and social success in the United States, rooted in the ideals of the Declaration of Independence. However, its meaning has evolved over the centuries. This project investigates contemporary perceptions of the American Dream and its evolution, questioning its tangibility in modern society.

Using primarily questionnaire-based research, we surveyed college students from various universities nationwide, representing diverse socioeconomic, gender, and racial backgrounds. The questionnaire explored current perceptions of the American Dream and the attributes associated with achieving it. Additionally, we plan to augment our data collection through in-depth interviews and archival research.

Our objective is to gain a deeper understanding of the modern interpretation of the American Dream. Initial findings suggest that historical trends persist, with white males often viewed as the quintessential embodiment of the American Dream. However, our analysis also reveals nuanced perspectives on the attainability of this concept across different demographic groups, considering factors such as the cost of living and efforts to enhance gender and racial diversity in the workforce. Our research sheds light on how individuals from diverse backgrounds interpret and pursue the American Dream in contemporary society.

Continue reading Emma Yorke »

Exploring Variations in Strength and Mechanical Properties of Lumbar Vertebrae in Eulipotyphla

This study examines potential strength variations among lumbar vertebral units, driven by functional trade-offs. Microindentation testing is employed to elucidate mechanical property discrepancies across distinct vertebral units. Hypothetically, shrew lumbar vertebrae features will be correlated with mechanical attributes, including stiffness and load-bearing capacity. We are predicting that the vertebrae may show adaptations in strength with a trend of increasing strength as one descends the vertebral column. This could be driven by the need to balance the mechanical demands of supporting weight and providing stability with the requirement for flexibility and mobility in the lower lumbar region. In other words, the transition from weight-bearing to facilitating movement prompts a distribution of strength to address these functional trade-offs, consequently yielding the observed pattern.

Continue reading Myleen Paulina Amendano »

Machine Learning to Detect Damage in Non-Linear Systems

Structural and mechanical systems wear and fatigue over time and managing these effects has proven to be a critical challenge for engineers. Monitoring damage in systems allows time, resources, and money to be properly allocated to repair or replacement. Many methods exist for damage detection in linear systems, but nonlinear systems have few methods available. Sensitivity vector fields (SVFs) are one way of detecting damage in nonlinear systems. SVFs quantify how nearby dynamic paths in phase space, called trajectories, separate due to damage. The magnitude of a sensitivity vector is expected to correlate to the amount of damage while the direction of a sensitivity vector can indicate the type of damage. This research used machine learning (ML) to expand SVF damage detection into the multi-parameter domain and to test its effectiveness on experimental data sets. Experimental data from a Chua circuit was processed in MATLAB and then the damage was classified using a stacked meta-learning model. A mix of support vector machines and random tree ensembles provided the best classification and regression model. The model was able to predict changes in all three system parameters with an MSE of less than 2%. Simulated data was created for a five-body mass-spring system driven by a chaotic signal. Accurate classification and regression results were obtained for limited damage scenarios, such as multi-level damage at one location or a fixed amount of damage at multiple locations.

Continue reading Alejandro Plata »

Exploring feminine expression and the aftermath of gender-related trauma in Romantic literature

This study delves into the intricate interactions between expressions of traditional femininity and the psychological complications of gender-based trauma in Romantic literature. Through a nuanced analysis of Mary Shelley’s “Valperga” and “Matilda,” alongside Julia Ward Howe’s “The Hermaphrodite,” this research project investigates how characters negotiate societal expectations, complex desires, and traumatic experiences shaped by gender roles.
In “Valperga,” the protagonist Euthanasia exemplifies a blend of feminine and masculine traits as she grapples with the conflict between political loyalty and love. Her ultimate demise underscores the struggle of individuals occupying undefined spaces within the Symbolic order. Similarly, Matilda’s narrative unveils the consequences of patriarchal dominance, leading to her self-destructive behavior rooted in complex and taboo love and the ensuing emotional turmoil.
Contrasting traditional gender binaries, “The Hermaphrodite” explores the journey of Laurence, an intersex individual navigating identity and societal expectations. Laurence’s liminality disrupts conventional norms, prompting questions about belonging and acceptance within the Symbolic order.
By applying Lacanian psychoanalysis, this research illuminates how rigid gender roles perpetuate emotional dependency and traumatic experiences. The analysis highlights the need for greater flexibility and diverse representation to address the shortcomings of the Symbolic order. Recognizing the complexities of gender expression is crucial in fostering healing and empowerment for individuals grappling with trauma-induced narratives.
This study contributes to a deeper understanding of gender dynamics in literature and advocates for transformative narratives that transcend traditional binaries, offering avenues for healing and agency in the face of trauma.

Continue reading Anna Marie Wingard »

Using AI in Assistive Technology to Support Effective Note-Taking Skills

In the ever-evolving landscape of engineering education, we are at a point where traditional instruction can be supplemented by AI to bridge the gap between current and best practices in the area of universal access. We have analyzed the Explicit Instruction Model, which matches well with STEM teaching approaches in higher education, with a Universal Design for Learning (UDL) lens to identify points in the instruction process where AI could support learning. From this activity, we determined that AI can facilitate learning through summarizing lecture content, identifying main ideas, developing checks for understanding, and providing supplementary resources for students. Once these features were identified, their efficacy was tested on three prominent generative AI platforms: ChatGPT 3.5, Gemini, and Bing AI. We created a prototype that analyzes a video of a STEM lecture and extracts a text transcript. The transcript data is then sent to the AI platform, which returns summaries, main ideas, checks for understanding, and supplementary resources pertaining to the lecture.

We are investigating more AI-powered tools, focusing on their ability to provide real-time feedback and adapt to individual student needs. Additionally, various metrics and assessment strategies are under consideration to allow educators to quantify the impact of AI on student engagement and learning. In the future, we plan to deploy the AI prototype to collect Bucknell student feedback. By drawing a clear connection between explicit instruction models, UDL, and AI, engineering instructors can create more effective learning experiences for their students.

Continue reading Maddy Kalaigian »

Chaotic Synchronization and Damage Detection

The general approach to the research was to attempt to build as much knowledge on chaotic systems and synchronization as possible in order to explore potential avenues for future research. Seeing as I was unfamiliar with the code and I needed to simulate the relevant chaotic systems, an introductory course to that syntax was one of the initial aims of the research. Familiarizing myself with differential equation calculations as well as various ways of displaying and interpreting the relevant data were essential skills I picked up in the wee stages of this research.
A literature review of relevant papers followed the introductory phase of the research where I looked for, read, and summarized salient aspects of academic articles in relation to chaotic synchronization and damage detection. Now armed with relevant information, skills, and equations I began to replicate previous works cited in articles in order to reconcile all the various skills I had annexed over the weeks and cement my understanding of them as a single analytical procedure.
I mostly worked in MATLAB and relied on the advanced integral calculator ODE45 to run most of my simulations. By simulating appropriately coupled chaotic systems I attempted to see if I could determine the difference between parameter values in the equations of the two involved systems (damage). I did this by evaluating one of the output component’s clearly defined properties. The result was that I found two ways of reliably determining the value of unknown parameters in the driving equation by comparing the output values:

1)The value of the deviation in the first peak or trough from the start line
2)The average of one of the predetermined outputs of the system
While the results varied for different chaotic systems, in most cases one or the other or both gave a good estimation of the value.

Aside from these findings I learned a few new math-oriented skills like how to calculate eigenvalues and determine the nature of a chaotic system based off it, how to write a state space equation and how to plot vector fields in MATLAB.

Continue reading Sethumte Asamoah-Nani »