Stanford University Student Journals
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You Are What You Eat: Chinese Ethnic Restauration in Paris as Identity Work
Full text linkThis study investigates the role of French-born Chinese restaurateurs in Paris as arbiters of identity who are redefining the cultural significance of Chinese cuisine for a predominantly non-Chinese clientele. Utilizing semi-structured interviews, the field study delves into how these ethnic entrepreneurs utilize their culinary concepts as a platform for cultural identity negotiation and challenging dominant cultural narratives. The primary research question(s) explored are: How do these restaurateurs use their mercantile and culinary strategies to stage diasporic identity and influence sociocultural dynamics? To what extent does internalization of dominant tastes (habitus) influence their staging of identity versus more pragmatic principles of instrumental rationality? The findings reveal that dining and food consumption extend beyond mere social activities to become venues for complex cultural negotiations, where ethnic entrepreneurs challenge and navigate cultural hegemonies and identity formation processes. The study underscores the nuanced role of ethnic cuisine in altering cultural perceptions and power dynamics within a multicultural urban context. This research suggests further comparative analysis across different diasporic communities and direct engagement with consumers and industry stakeholders to enrich understanding of the broader sociocultural implications of ethnic culinary entrepreneurship
Embracing Genetically Modified Crops for Global Prosperity
Full text linkThis paper advocates for the global implementation of genetically modified crops, highlighting their substantial benefits and addressing prevalent controversies. The primary research question explores their impact on agricultural productivity, nutritional enhancement, and pesticide reduction. Utilizing a comprehensive literature review, the study reveals significant increases in crop yields and productivity, particularly in developing countries. It also emphasizes the nutritional benefits of biofortification, exemplified by β-carotene-enriched rice to combat vitamin A deficiency. Furthermore, the paper discusses their environmental advantages, such as reduced pesticide use and associated health risks. Addressing safety concerns, the research indicates a broad scientific consensus on the safety of GM foods, supported by major health organizations. Despite ecological concerns about biodiversity, studies show minimal adverse effects. The paper concludes by highlighting future prospects in genetic engineering, including cisgenesis and genome editing, which promise to further revolutionize agriculture. These findings suggest that genetically modified crops are a crucial tool for achieving global agricultural prosperity and call for policy reforms and public education to support their adoption
Breaking the Silence with Direct-Speech Brain Computer Interfaces: Centering Communicative Disability in Ethical Recommendations for Mitigating Algorithmic Bias
Full text linkDespite growing scholarly attention to the ethical implications of direct-speech brain-computer interfaces (BCIs), there remains a lack of concrete guidance on how to address these concerns—particularly for users with communicative disabilities. As neuroengineering continues to advance alongside developments in artificial intelligence and machine learning, the need for clear ethical frameworks and standardized protocols becomes increasingly urgent. This paper investigates the core ethical challenges of direct-speech BCIs for individuals with communicative disabilities, identifying algorithmic bias as a central and underexamined issue. Through analysis of existing neuroethical standards, policy proposals, and international legislation related to AI and neurotechnology, the paper exposes critical gaps in current guidance. Drawing from disability studies and related fields, it argues that mitigating bias and ensuring equitable BCI development requires a broader, more inclusive understanding of language and a commitment to user-centered design
Unmasking AI:: Why Ethical Tech Is Everyone’s Responsibility
Full text linkThis review examines Unmasking AI: My Mission to Protect What Is Human in a World of Machines by Dr. Joy Buolamwini, a leading voice in AI ethics and founder of the Algorithmic Justice League. The reviewer positions Buolamwini’s work as both a critique of current AI practices and a call to action for inclusive, ethical, and interdisciplinary approaches to technology.
De-Identified Medical Datasets and the 2025 Readiness Gap:: Toward Equity, Scale, and Trust in Foundation Model Training
Full text linkFoundation models (FMs)—large-scale machine learning models trained on vast, diverse datasets—are reshaping the future of medical AI by powering diagnostic tools, clinical decision systems, and health information summarization. Sometimes referred to as large language models (LLMs) when applied to text, these models are increasingly deployed across clinical contexts. However, the de-identified datasets that form the backbone of FM training are often outdated, demographically limited, and difficult to access. These limitations raise profound concerns about fairness, scientific validity, and the potential for harm—particularly for marginalized populations underrepresented in training data. This paper argues that current de-identified datasets are not adequately representative or accessible for building trustworthy AI in healthcare. It critiques the prevailing assumption that de-identification alone ensures ethical readiness, showing instead how it can obscure structural biases and entrench inequality. Drawing on recent research and emerging technical and policy solutions—including synthetic data generation, automated de-identification, and global benchmarking—this paper explores what it means for datasets to be “2025-ready.” It proposes a new standard for responsible dataset design, grounded in demographic transparency, equity-centered governance, and inclusive participation in medical AI development
Towards Safe and Ethical AI
Full text linkAs large pre-trained language models grow prevalent, efforts in preventing biased and hateful outputs related to race and gender are increasingly critical. Since initiatives are scattered and fragmented, this review outlines the latest methods for measuring safe, ethical AI and discusses their limitations. By spotlighting the proper utilization and challenges of state-of-the-art methods, this review seeks to foster continuing discourse and innovation among both technical developers and non-technical policymakers
If Bears Can Get Insecure, Imagine Being a Woman!: How Dieting Language Becomes Moralized and Gendered
Full text linkThe purpose of this microfilm was to share a foundational text from my literature review that I used to prepare for my PWR 2 Research-Based Argument. I explored how dieting culture and the Western obsession with thinness is an inherently racialized, gendered, and classist process. The use of my stuffed bear (endearingly referred to as Desmond) was a way to transform a difficult, uncomfortable, and deeply personal topic into something more lighthearted and engaging. The premise of a bear who also struggles through insecurities is meant to highlight the absurdity of societally imposed beauty standards: Bears are bears. They should not be defined or hierarchized based on ambiguous conventions of beauty. In the same vein, humans are humans, and what makes us beautiful is our capacity to breathe, to think deeply about ourselves and our world, and our ability to be in community with each other. These standards and insecurities did not emerge out of a vacuum, but instead were deliberate processes that were organized for several centuries in order to justify a certain social and economic hierarchy.
As I recorded myself in dim lighting in my dorm room, moving my bear around on my bed and sitting in a corner to record Desmond\u27s voiceovers, I felt prior insecurities and experiences get projected on Desmond himself. It was cathartic to release this energy onto my stuffed bear, to know that for now, Desmond was holding on to these experiences as well. I hope, too, that those who may be struggling with feelings of insecurity or inferiority are also able to critically step back and analyze the material and historical conditions that led them to feel this particular way; then to ask and think: What if my beloved stuffed bear was struggling, too?
References:
Hesse-Biber, S., Leavy, P., Quinn, C. E., & Zoino, J. (2006). The mass marketing of disordered eating and Eating Disorders: The social psychology of women, thinness and culture. Women’s Studies International Forum, 29(2), 208–224. https://doi.org/10.1016/j.wsif.2006.03.007
Wolf, N. (1992). The beauty myth: How images of beauty are used against women (1st Anchor Books ed). Anchor Books
Where the Grass is Greener: Urban Green Spaces, Sustainability, Mental Health, and Social Inequity
Full text linkFrom meticulously groomed parks and yards to forgotten patches of untamed growth, Urban Green Spaces (UGS) exist on a spectrum. Curated landscapes have a long and complex history, reflecting societal ideals and embracing nature while, paradoxically, suppressing biodiversity and deepening socioeconomic divides. To maximize aesthetics and usage, chemical treatments and intensive fossil-fuel upkeep methods are frequently employed. This harms pollinators, disrupts ecosystems, and can introduce intended beneficiaries to dangerous contaminants and pollutants. In contrast, Informal Green Spaces (IGS) such as abandoned lots, small community gardens, and similar lightly tended spaces nurture greater ecological diversity by allowing native species to thrive. And yet, these spaces can face legal barriers and stigmatization, particularly in less affluent neighborhoods where IGS are more common. Socioeconomic disparities are further exacerbated when green initiatives increase property values and displace residents, a phenomenon known as eco-gentrification. To address this gap, this paper examines collaborative urban planning methods and science-based planting strategies that integrate community voices and environmental needs. Such a route emphasizes the potential for UGS to enhance physical and psychological health, mitigate climate effects, and strengthen community bonds
A Hydrokinetic Design to Alleviate Freshwater Demand
Full text linkFreshwater scarcity is a growing global concern, with the urban population facing water shortages projected to double by 2050, affecting up to 2.4 billion people. Desalination, a potential solution, currently accounts for just 1% of the world’s drinking water due to its high cost, energy intensity, and reliance on fossil fuels. Traditional desalination plants consume over 200 million kilowatt-hours daily and contribute significantly to greenhouse gas emissions, which is counterproductive in addressing water scarcity exacerbated by climate change.
This project proposes a sustainable and low-cost desalination approach using a hydropower energy generation system that harnesses the kinetic energy of seawater. The system employs ram pumps to lift seawater to a higher-altitude reservoir. Stored potential energy from the elevated seawater is converted into electricity through a turbine. This selfsustaining system uses the generated electricity to desalinate wastewater produced during the pumping process, eliminating the need for external energy sources. It is particularly suitable for coastal areas, where 40% of the global population resides within 100 kilometers of a coast.
By utilizing a renewable and perpetually available energy source, this approach reduces reliance on fossil fuels and minimizes environmental impact, rendering it a viable and scalable solution to the global freshwater crisis. The system\u27s adaptability to various coastal environments enhances its potential for widespread application
The Incorporation of Artificial Intelligence in the Identification of Neurological Disorders
Full text linkWith the rise of neurological disorders among a wide age bracket today, efficient and accurate diagnosis has faced some challenges. Due to the amount of time needed to observe the physiological symptoms of the brain as well as the behavior of the patient, certain neurological disorders can be mistaken for another. This led to the initial research question: To what extent can AI limit the amount of misdiagnoses by improving efficiency and reducing diagnosis time for neurologists in the United States? This study focused on monitoring three different artificial intelligence models and their efficiency to provide an initial diagnosis of a disorder based on an MRI scan. The three models that were compared were the Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN). Due to the similarity between the functioning of the CNN model and the typical human brain, it was hypothesized that the CNN model would be the most efficient. After running the brain scan through each model multiple times and averaging the data, it was found that the Convolutional Neural Network had the quickest response time and the most accuracy compared to the other models. This response time was based on the trained AI model’s ability to make a diagnosis. A multiclass output was utilized for the final diagnosis results. Further implementation of AI models in the diagnosis process, especially CNN models, can lead to significant improvement in the field of neuroscience.