California State University, Monterey Bay
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Health Care in Women’s Carceral Spaces
Full text linkCanada has had a long history of mistreating incarcerated women in their jails. The carceral system was built to fit around the needs of men, neglecting to consider the various health issues that only women face. For many years. federally imprisoned women were held in one deteriorated prison, the Prison for Women (P4W), and were held in abhorrent conditions and experienced many acts of violence. Despite the opening of five more prisons and funding for various programs for women, incarcerated women continue to experience violence and health crises, leaving imprisoned women extremely vulnerable to mental and physical distress. In particular, there is little access to contraceptives and sexual health care, due to a lack of staffing, mistreatment by correctional guards and long waitlists. It is especially important to consider incarcerated mothers, the distress women face while giving birth in prison, as well as the distress they face when separated from their children. Utilizing research from community organizations and academic sources, this literature review will examine how the federal government is neglecting the sexual and maternal care of incarcerated women in Canada, and how this leads to greater risk in health for both incarcerated women and the broader community outside of these spaces
Machine learning for asphaltene polarizability: Evaluating molecular descriptors
Full text linkAsphaltenes are complex polycyclic organic molecules in crude oil that readily aggregate and precipitate under varying thermodynamic conditions. Their structural heterogeneity influences key physicochemical properties, including solubility, stability, and reactivity. Molecular polarizability, a crucial property governing intermolecular interactions and electronic behavior, remains challenging to predict due to this structural diversity. This study employs machine learning models to predict isotropic polarizability using two sets of molecular descriptors: WHIM and GETAWAY. A dataset of 255 asphaltene structures was analyzed using stratified sampling, generating 10 independent training (80 %) and testing (20 %) splits. The Wolfram Language’s Predict function evaluated multiple machine learning algorithms—including Random Forest, Decision Tree, Gradient Boosted Trees, Nearest Neighbors, Linear Regression, Gaussian Process, and Neural Network—through an automated model selection process, serving as an AutoML framework. Linear regression was the best-performing model in 9 out of 10 splits for GETAWAY descriptors. GETAWAY-based models achieved an average mean absolute deviation of 0.0920 ± 0.0030 and standard deviation of 0.113 ± 0.004, significantly outperforming WHIM-based models (MAD = 0.173 ± 0.007, STD = 0.224 ± 0.008) with paired t-tests confirming statistical significance (p \u3c 0.001). While R² values were reported, their interpretability was limited by heterogeneity and narrow property ranges in some test sets. These findings demonstrate the effectiveness of AutoML-guided approaches for predicting molecular properties and identify GETAWAY descriptors as a robust, efficient basis for polarizability prediction. Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications
Beyond self-care: Developing a climate survey for school psychology programs
Full text linkGraduate program climates are an essential part of training, professional development, and identity for future school psychologists. One of the challenges of understanding program climate is how to measure such a construct so that programs can facilitate welcoming and trans- formational spaces for learning and critical thinking. This study aimed to develop and initially validate a measure of graduate students’ perceptions and experiences of school psychology program climates, the Climate Assessment for Relationships and Equity in School Psychology. Results of an exploratory factor analysis from a sample of 212 school psychology graduate stu- dents revealed a four-factor structure: (a) program dynamics and psychological safety, (b) diversity, equity, and inclusion, (c) peer support, and (d) resources. Of these factors, graduate students rated peer support most favorably and diversity, equity, and inclusion as most lacking, indicating a continuing need for programs to provide opportunities and training to engage in program and individual accountability as they relate to social justice issues. Implications for graduate student and faculty advocacy to improve climate in school psychology graduate pro- grams are provided
Mapping Horizon Limits Using Topographical Reference Points for Determining Pre-Columbian Circum-Caribbean Indigenous Interisland Contact Routes
No full textExisting archaeological studies have suggested an extensive network of ongoing human travel operated between the Bahamas, the Greater Antilles, and the Lesser Antilles before and at the time of European contact in 1492. Additional evidence prompts some scholars to propose expanded contact of these areas with Mesoamerica, the rest of Central America, the northern coast of South America, and the Florida peninsula, resulting in an interconnected circum-Caribbean region. A variety of economic, political, and social influences stimulated this desire for sea and ocean movement, including migration, trading, tribute, raiding, and social connectedness. Geographic and oceanographic factors such as distance between islands and favorable winds and currents played a major role in determining the location of crossover routes, while shoreline reefs might help or hinder canoe progress and thus the selection of sea routes.
The purpose of this study is to identify and map a limited group of historically referenced and scholarly suspected routes of travel via canoes, integrating topographic, highest points of natural terrain, and geographic locational realities, nearest points of contact, to provide a sample of preferable Lucayan, Maya, Taíno, and Carib pre-Columbian interisland contact routes and to investigate possible Caribbean island travel sea avenues connecting mainland areas such as Florida, Mesoamerica, and Venezuela. The cartographic analysis of a select number of maps showing the distance along recommended interisland routes highlights the limited approach of this study, with topography features and ensuing horizon limits, distances to the highest island elevations, supporting or restricting designated routes, and recommended journey times
Green-curious retail investors and unmediated interactions with GenAI
No full textThis paper examines how generative AI (GenAI) tools such as ChatGPT respond when green-curious retail investors seek investment advice through unmediated, prompt-based interactions. Motivated by climate concern, these investors may lack financial or climate literacy and may prefer GenAI tools to human advisors, robo-advisors, or platform-integrated systems. Drawing from prior research on investor interactions with GenAI and on information critical for making green investment decisions, we conducted a prompt-based experiment using ChatGPT-4o and ChatGPT 5. Results show that ChatGPT encouraged green investing across all queries, but the inclusion or omission of decision-critical information varied widely. Key considerations, such as lower expected returns relative to other investments, greenwashing and confusing label warnings, and distinctions between ESG and climate-aligned products, often were surfaced only when explicitly prompted. These findings highlight the importance of GenAI literacy along with climate and financial literacy in shaping investor outcomes. We propose a research agenda to examine how GenAI tools inform or mislead retail investors in unstructured settings and to guide investor education, system design, and policy development for responsible GenAI use in retail finance