Open Research Oklahoma (Oklahoma State Univ.)
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Toward a "faceless deity": Jean Toomer and "The Blue Meridian" as transreligious utopia
No full textMuch scholarly attention has been directed to biracial writer Jean Toomer’s 1923 novel Cane, particularly his transgressive approach to racial identity amid early 20th-century racial purification projects. To contend with his competing identities, Stephanie Hawkins argues that Toomer adopts a philosophy of transracialism in which racial identity becomes legible only through its transgression. Yet Toomer’s transgressive approach to race encompasses political categories like gender, nation, and, as I explore in this paper, religion.
Despite extensive critical engagement with Cane, much less attention has been paid to Toomer’s post-Cane writings, which reckon with religious identity and ultimately culminate in his final published poem, “The Blue Meridian” (1936). In the poem, Toomer suggests that racial unification is only possible through religious unification, and it is only in tandem that the emergence of the “new America” Toomer envisions is possible.
In this paper, I extend Hawkins’s notion of transracialism into the religious sphere to argue that Toomer articulates an ontologically complex, politically subversive, and only recently defined sense of transreligiosity. Drawing on Anastasios Panagiotopoulos and Eugenia Roussou’s 2019 concept of transreligiosity — defined as a deterritorialized religious orientation shaped by global networks — I situate Toomer’s evolving spirituality within the broader context of 20th-century cross-cultural religious exchange.
In my close reading of "The Blue Meridian," I argue that Toomer constructs a “New Order of Religion” to dissolve religious boundaries and connect his established transracialism to a unified politics of transgression and create a foundation for political life. This politics, I argue, serves to disrupt entrenched political categorizations, like race, nationality, and religion, and lays the foundation for a utopian world.
In doing so, I aim to animate Toomer’s utopia through a coherent, even if idealistic, political framework and give voice to his post-Cane work. While acknowledging the contradictions and limitations of his vision, I explore its broader implications for contemporary discussions on race, religion, and identity
On-farm profitability of cover crops in the United States: A meta-analysis
No full textThis study addresses the lack of consensus on the economic viability of cover crops for U.S. farmers by analyzing their on-farm operational profits. Using data from 35 studies, we estimated the agronomic and economic impacts of planting cover crops before the main crop. We employ mixed-effects meta-regressions with a multiplicative variance to adjust for variability of cover crop effects, and random effects to adjust for heterogeneity and within-study correlation. Our agronomic findings indicate a modest yield increase for corn, sorghum, and cotton when grown after cover crops, but slight yield reductions for wheat and soybeans. However, economic analysis demonstrates that cover cropping generally results in net profit losses. The magnitude of these losses is influenced by the type of main crop grown after the cover crop. These findings underscore that financial incentives to encourage the adoption of cover crops in the U.S. would be required in order to obtain the environmental benefits of cover crops, such as carbon sequestration
Race, gender, and teacher autonomy: A quantitative analysis of Black and White educators
No full textThis thesis explores how race and gender shape perceptions of teacher autonomy and how autonomy, in turn, predicts job satisfaction among Black and white educators in the United States. Against a backdrop of increasing political and community pressures—such as curriculum censorship, book bans, and legislative attacks on discussions of race and gender—the study examines how these factors influence teachers’ professional agency. Drawing from intersectionality theory and job quality literature, it frames autonomy as both a structural feature of teaching and a site of inequality.
Data were collected via a Qualtrics survey distributed through Prolific (N = 550). The analytic sample included only respondents who identified as Black or white and worked primarily in teaching roles. Two composite measures—teacher autonomy and job satisfaction—were constructed using Likert-style items adapted from the Schools and Staffing Survey (SASS). Regression models incorporated a four-category intersectional race-gender variable (white men [reference], white women, Black men, Black women) along with covariates for subject taught, grade level, school type, political context, peer racial match, and support during the first year of teaching.
Results indicate that autonomy is a strong and statistically significant predictor of job satisfaction. However, race-gender identity was not significantly associated with either autonomy or satisfaction, suggesting that structural or contextual factors may outweigh identity alone. Notably, greater autonomy was reported by teachers in private schools, arts subjects, higher grade levels, and those who racially matched with their coworkers. Teachers in charter schools and elementary grades reported lower autonomy. First-year support was associated with greater satisfaction, highlighting the importance of mentorship and institutional integration.
These findings underscore the complexity of professional autonomy as shaped by institutional context, not just individual identity. The lack of significant race-gender effects may reflect measurement limitations or intra-group variability. Nevertheless, the study affirms autonomy as a central feature of job quality and a key mechanism for promoting teacher satisfaction. It concludes with recommendations for future research that incorporates more inclusive sampling, interactional modeling, and mixed methods approaches to deepen understanding of how structural inequalities shape teacher experience
Are gender diverse individuals natural linguists?
No full textThis study looks at how four folks who identify as gender diverse view their gendered language performativity. As people in a marginalized community– especially in the South– their language use is overlooked as well as their perception of their language use. This is especially unique in Oklahoma. Oklahoma’s unique positionality of having multiple dialectal regions allows for Oklahoma natives to have a wider linguistic toolbox. As well as linguistics, Oklahoma’s conservative politics affect how people in the trans and gender diverse communities (a marginalized one) use gender performativity. Some of the participants had attributes of natural linguists and some of the participants did not exhibit the same nuanced understanding. None of the participants discussed how the different dialect regions of Oklahoma assisted or affected their linguistic gender performativity. Most of the participants brought up how the political background of the state affected their performativity. Various factors, such as politics, linguistic background, time in identity, affect how gender diverse individuals perform as natural linguists
Publications in agricultural economics, Oklahoma State University, July 1, 1971 - June 30, 1972
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Auditory effects of ear notching on mice (Mus musculus)
No full textEar notching, a common identification and genotyping method in research, involves creating small hole punch biopsies in the pinna. The pinna plays a key role in sound localization, and it is unknown if altering the ear morphology through the ear notching technique may affect ear auditory processing. This study investigates the effects of ear notching on binaural hearing utilizing head-related transfer function (HRTF) measurements in preserved adult male Mus musculus (house mouse). Mice were tested in a sound-attenuating chamber with a rotating speaker array and probe tube microphones to capture interaural timing differences (ITDs) and interaural level differences (ILDs) across progressive ear notch patterns. ITDs remained largely unaffected by superficial pinna modifications across conditions, suggesting preserved timing-based localization. In contrast, ILDs were noticeably altered, particularly at higher frequencies, with disrupted patterns continuing as notches increase. These results suggest that while ear notching does not impact lower-frequency timing cues, it can degrade high-frequency level-difference cues, which could reduce sound localization precision. These findings have broad implications for auditory research involving ear-notched mice and may extend to other species identified by ear notching
Integrating GIS, field surveys, and machine learning models for enhanced culvert inventory management in Oklahoma.
No full textCulverts are critical in transportation infrastructure and provide hydraulic conveyance beneath roadways. However, factors such as culvert age, urbanization, and climate change are believed to contribute to growing vulnerability. As culverts deteriorate over time, it is expected that their structural integrity and hydraulic capacity diminish, leading to potential road-stream crossing failures, increased maintenance cost, and adverse environmental impacts. To address these challenges, this study integrates Geographic Information Systems (GIS) analysis, field assessments, and machine learning (ML) models to improve culvert inventory management, assess culvert conditions, and develop data-driven methods for predicting culvert deterioration. The National Bridge Inventory (NBI) database is the primary data source for existing culverts in the United States. GIS analysis was conducted to identify vulnerable culverts for field visit. Field surveys were conducted at 100 vulnerable road-stream crossings with culvert conditions ranging from fair to failed. These site inspections involved verifying culvert characteristics, assessing culvert condition, and evaluating the feasibility of retrofitting or replacement. A standardized data collection protocol was implemented using a Microsoft form that incorporated elements from the Southeast Aquatic Resource Partnership (SARP), the Specifications for National Bridge Inventory (SNBI), and the Pavement Surface Evaluation and Rating (PASER) system. In addition to GIS analysis and field assessments, ML models were developed to analyze and predict culvert deterioration. The ML analysis was conducted on 3,191 culvert entries to identify culvert features that could affect culvert conditions. The dataset was split into 80% training and 20% testing. Sixteen ML models were evaluated across four data categories, including refined data, age-adjusted data, filtered data, and complete data. Gradient Boosting classifier achieve the highest accuracy of 82.66% on refined data and 82.48% on age-adjusted data, Extreme Gradient Boost 82.46% on filtered data, and CatBoost classifier 81.78% on complete data. This study proposes a comprehensive methodology for culvert inventory and inspection prioritization using the state of Oklahoma as a case study. The findings from this study informed the design of an Excel-based culvert prioritization tool. The tool can help agencies identify culverts in need of inspection, retrofitting, or replacement. It is anticipated that the findings from this work will enhance culvert asset management, improve culvert evaluation methods, and better inform decision-making for infrastructure resilience. The integration of GIS analysis, field surveys, and ML models provides a scalable framework that can be adapted in other regions
Uncertainty resilient visual inertial odometry in dynamic movement of micro aerial vehicles
No full textThis dissertation presents a novel bio-inspired approach to Visual-Inertial Odometry (VIO) for autonomous mobile robots, with a particular focus on Micro Aerial Vehicles (MAVs) operating in GPS-denied environments. The research addresses the critical challenge of achieving a balance between computational speed, estimation accuracy, and interpretability in VIO systems while maintaining resilience to sensor anomalies and environmental uncertainties.
Drawing inspiration from the human vestibulo-ocular reflex (VOR), we introduce a stateless, pre-integrated VIO framework that fundamentally reimagines how visual and inertial data are fused. Unlike traditional stateful approaches where errors accumulate over time, our system independently integrates IMU measurements for each camera frame interval, effectively resetting the inertial estimation at every step. This bio-inspired architecture confines erroneous sensor readings to isolated frames, preventing long-term error propagation.
The proposed system incorporates four key innovations: (1) a robust IMU anomaly detection and data synthesis framework using Random Forest classification and lightweight deep learning models; (2) a pre-integrated visual odometry component employing IMU-based filtered sparse optical flow; (3) enhanced pose estimation through triangulation; and (4) a modular Fully Connected Network (FCN) approach for pose refinement that processes each degree of freedom independently.
Extensive experimental evaluation on benchmark datasets demonstrates the system's superior performance compared to state-of-the-art methods. Our approach achieves a 38.6\% reduction in Absolute Trajectory Error compared to the next best method, nearly 50\% lower rotational error than filter-based approaches, and maintains over 60\% pose estimation capability even with significant IMU latency (200ms) where conventional methods fail completely. Processing speeds of 15.625 to 31.25 microseconds per step enable real-time performance on standard hardware.The integration of biological principles with advanced engineering techniques has resulted in a VIO system that simultaneously addresses the longstanding challenges of accuracy, computational efficiency, interpretability, and resilience to sensor inconsistencies. This research advances autonomous navigation capabilities for MAVs in challenging environments and establishes a foundation for future bio-inspired approaches to robotic perception and state estimation
Red, white, and blues: The rise and reign of an American demagogue
No full textThis project investigates the fascist demagoguery and propaganda of Donald Trump throughout his life, with special focus on the years since he announced his candidacy for President in June 2015. While a wealth of scholarly attention has been paid to various elements of Donald Trump’s rhetoric, this project pays special attention to areas that, to date, have been understudied in Rhetoric and Writing Studies (RWS): visual rhetoric and his defense of the culture of Whiteness through his use of the rhetoric of Lynching, as informed by Dr. Ersula Ore’s book, Lynching: Violence, Rhetoric, and American Identity. In order to accomplish these goals, I first examine the visual rhetoric of Donald Trump’s 2016 Presidential Campaign. This examination is followed by an analysis of various written and spoken texts made by Trump throughout his career in the spotlight. Finally, this dissertation features an analysis of the propaganda of one of the most prominent right-wing political think tanks, The Heritage Foundation, and their propaganda arm, Project 2025 through an examination of their Mandate for Leadership text. Ultimately, it was the goal of this project to conduct an investigation of the rise, reign, and future results of a modern, American demagogue. By studying the rise, continued popularity, and future policy proposals of Donald Trump and his far-right political allies, this dissertation serves as an in-depth look into the rhetorical strategies, tools, and displays that enable(d) Trump’s rise. As a result of the analyses made in this work, I introduce a new term for Rhetoric and Writing Studies, Proximity Demagoguery, which I define as the defense of an identity that is not held by an individual, but is still defended and used as a cultural binding agent to promote the creation of identity groups in the pursuit of identity politics