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High School Arts Enrollment among Students with Disabilities
Get PDFArts experiences can promote both social inclusion and identity development, areas where adolescents with disabilities face difficulties. Adolescents most commonly access the arts through school classes; however, students with disabilities may be less likely to enroll in these classes compared to students without disabilities. This study analyzed data from a primarily low-income, ethnically diverse dataset of high school students (N = 29,339) to determine whether the odds of enrolling in an arts class in high school depends on whether a student has a disability. This study analyzes the relationship between both disability status and disability type and the odds of enrolling into different types of arts classes (any arts, music, dance, drama, visual arts). Chi squared tests determined whether there were significant differences in the raw arts enrollment rates between students with and without disabilities, and multivariate logistic regression models tested whether these differences were significant when accounting for the effect of relevant covariates (demographic variables and prior academic performance) that relate to arts enrollment. Results indicated that students with a disability status were less likely to enroll in any arts class, and for all arts types except for visual arts. However, when accounting for covariates, students with a disability status only had reduced odds of enrolling in music and dance classes. The disability type most commonly associated with reduced odds of enrolling in arts classes was intellectual disability, followed by autism and speech/language disability. Other research shows that such students are more likely to be educated in segregated classrooms and have high support needs, and a possible explanation for their reduced enrollment of arts classes is that they often don’t have the opportunity to be included in arts classes due to institutional practices and teachers without training and resources for inclusion. Implications for policy and future research are discussed below
What’s the Secret Recipe for Persuasive Message Content Success?
Get PDFThe science of persuasion remains an area of inquiry largely driven by siloed research programs that rarely get tested together. Many theories do not tell us what works best. We all believe more persuasive messages ought to result in more behavioral engagement; such a relationship, however, might be tempered by a host of other factors. To address the question “what works?,” I conducted a three-staged study. First, I accumulated 556 message content qualities that experts have linked to persuasive success. Second, I collected message and persuasion data from social media to serve as my data source. Through the course of six weeks, I captured 1410 unique text-only messages that attempted to persuade the audience to take action that could be tracked (e.g., like, reply, retweet, and/or quote). Both the message content (predictors) and the behavioral data (outcomes) served as the basis for the next stage. Finally, I used a large language model (LLM) to “score” the messages according to the qualities identified in the first stage. The LLM was prompted to score every message on an 11-point (0-10) scale for each quality multiple times. In total, 1196 messages were scored ten times on 556 qualities. Once scored, I reduced the message quality ratings from 556 to 15 latent variables (via exploratory factor analysis). These latent variable scores then were used as predictors of the outcomes. The result from all three stages was an observational, longitudinal study that helped identify three consistent factors that predicted persuasive behaviors. My aim here was to create a recipe for best persuasion practices. Behavioral outcomes from persuasive social media messaging occur routinely (48.6% of my sample achieved a desired response) and are dependable enough to be predictable by message content qualities (pseudo-R2 = 0.25). With relatively high accuracy (74.4%), a logistic regression model predicted whether any message would persuade at least one user to engage. Despite those results, a simple, generalizable recipe does not exist, but there are grains of truth in almost every persuasion model available today - yet, most are wrong. Theoretical advances aside, the main contribution from this work is the application of LLMs for scoring observational data quickly and inexpensively. The implications of these methodological approaches may offer far greater heuristic value to the scientific community beyond the theoretical contributions
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 3521.01
Get PDFThe reason for this study was to confirm whether TOI- 3521.01, detected by the Transiting Exoplanet Survey Satellite (TESS) is in fact an exoplanet. We finalized this finding by separating the Dark and Flat images, data-reduced the sciences, aligned the plate-solved images, and placed multiple apertures on the previously aligned images. We received ground-based observations of TOI - 3521.01 and were expected to utilize multiple programs to classify this science. Through our model, we were unable to identify TOI - 3521.01 as an exoplanet, due to lack of sufficient data to properly finalize the science (Oxford Academic et al. 2022)
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 3873.01
No full text“This study investigates TOI 3873.01, an extra-solar planet candidate discovered by the Transiting Exoplanet Survey Satellite, or TESS for short. Despite TESS having significantly contributed to exoplanet research, very little to no prior research has been done on this object. Data reduction techniques such as flat and dark calibration were used in AstroImageJ to produce scientific images; along with aperture photometry and light curve analysis. The findings suggest that a hot Jupiter exoplanet orbits the star UCAC4 801-022341, supporting TOI 3873.01’s exoplanet status. Also, the calculated ESM of this exoplanet exceeded the projected threshold for exoplanets deemed qualified for JWST spectroscopic follow-up. This study contributes to the growing body of exoplanet research and demonstrates the value of follow-up observations for TESS candidates. The confirmation and characterization of TOI 3873.01 will hopefully add to the diverse understanding of exoplanets and may help guide future discoveries for potentially habitable worlds outside the solar system.
Optimal Subsampling for Logistic Regression Models: A Case Study
Get PDFUtilizing a large dataset that is representative of the current used car market allows for different statical methods to provide insight into predicting factors affecting prices. The study used three different methods IBOSS, BLB, and Random Sampling because these are believed as good prediction methods for large datasets and subsampling. The logistic regression model was compared with the different methods having subsampled the large dataset through the tuning of different parameters. The results from the analysis were measured by the Number of Active Coefficients, Maximum Absolute Error (MAE), Maximum Length, and Prediction Accuracy. These metrics were chosen because they allowed comparing the different subsampling methods to one another. The results from the study came to show that there was varying result amongst the different methods. The random sample method came to show it was the weakest of the three methods. The difference between IBOSS and BLB was that one was more precise and the other had better MAE
"UN LUGAR MUY ESPECIAL: NAVIGATING THE TENSIONS BETWEEN PARENS PATRIAE, 'LEGAL VIOLENCE,' AND THE RIGHTS OF CENTRAL AMERICAN UNACCOMPANIED MINORS IN THE U.S. LEGAL SYSTEM"
Get PDFThis dissertation explores the legal and social frameworks that govern the rights of unaccompanied Central American minors within the U.S. legal system. Using federal court rulings as a primary unit of analysis, the study examines the interplay between the legal doctrine of parens patriae and the sociological concept of 'legal violence.' The research is framed within a socio-jurisprudential context and systematically analyzes institutional practices and sociopolitical factors impacting these minors' experiences. Key research questions addressed include: How has the legal doctrine of parens patriae been established and interpreted by the courts in relation to unaccompanied minors? How do U.S. legal frameworks and court rulings provoke and perpetuate 'legal violence' towards unaccompanied minors, particularly through the actions of the Office of Refugee Resettlement (ORR)? The dissertation reveals that current legal structures simultaneously protect and harm vulnerable populations, underscoring the need for refined legal approaches that recognize and safeguard the rights of unaccompanied minors while addressing broader migration challenges. The study advocates for the establishment of a sociology of unaccompanied minors as a distinct field, emphasizing the importance of specialized research and policy development. Additionally, it calls for the creation of a stand-alone agency dedicated to the care of unaccompanied minors, separate from the ORR, to provide holistic and culturally appropriate services. The dissertation also supports the ratification of the Convention on the Rights of the Child (CRC) by the United States, to enhance protections for all children within its borders. This research contributes to sociological literature by highlighting the complexities of legal protections in migration contexts and the need for systemic changes to uphold the dignity and rights of unaccompanied minors
Towards a theory of Self Calibrating Sensors
Get PDFWe examine the impact of inference models on uncertainty when using continuous wave Optically Detected Magnetic Resonance (ODMR) measurements to infer temperature. Our approach employs a probabilistic feedforward model designed to maximize the likelihood of observed ODMR spectra through automatic differentiation. This model achieves a state-of-the-art prediction uncertainty of ±1 K across a temperature range of 243 K to 323 K. We show that, for out-of-sample data within the training temperature range, data-driven methods can reduce uncertainty by up to 0.67 K without incorporating expert knowledge of the spectroscopic-temperature relationship. However, the probabilistic model demonstrates superior robustness and generalizability and in contrast, data-driven methods exhibit up to ten times greater uncertainty when tasked with extrapolating beyond their training data range. The Probabilistic feedforward model motivated our development of a mathematical theory of self-calibrating sensors. We operate under the implicit assumption that sensors inevitably introduce slight disturbances while measuring the very phenomena they are designed to monitor. We perturb the dynamics of the system, proposing that these minor perturbations, such as those introduced by the sensor itself, can enhance observability. In a system with two states and a single observation, we show that, under certain conditions, the uncertainty in estimating the system's states is inversely proportional to the size of a small perturbation applied to the system. The one-dimensional observation case guided us to go to higher dimensions, and when the number of observations is only one fewer than the number of states we find similar results. However, for even fewer observations we illustrate significant additional challenges. Ultimately, this dissertation shows that self-calibrating sensors provide a robust solution for maintaining accuracy and reliability in the face of hidden state uncertainty and environmental fluctuations
CMS Pay for Performance Programs and Associated Hospital Characteristics
Get PDFThe three largest Pay for Performance (P4P) Center for Medicare & Medicaid Services (CMS) programs - the Hospital Value-Based Purchasing Program (HVBP), Hospital-Acquired Condition Reduction Program (HACRP), and Hospital Readmission Reduction Program (HRRP) - have collectively improved patient care and safety. However, all three programs have also been criticized for disproportionately penalizing certain kinds of hospitals. Evidence suggests large, teaching hospitals serving poorer populations are more likely to incur fines compared to other program participating hospitals. Few studies have looked at multi-policy penalization patterns amongst subjected hospitals. No studies have yet accounted for non-CMS patients while studying these programs, nor controlled for the presence of a transplant center within a given hospital. This study directly fills these literature gaps primarily in two ways. First, P4P program correlations estimate how these three programs interact with one another. Second, a series of regressions estimate what hospital characteristics associate to each P4P program penalization, which include novel introductions of non-CMS patients and transplant centers. The analysis utilizes 2016 data from New York hospitals, sourced from the CMS, American Hospital Association (AHA), and Healthcare Cost and Utilization Project State Inpatient Databases (HCUP-SID). In total, 144 of the 172 New York hospitals included in this study participate in at least one P4P program. Spearman correlations confirm speculation of significant penalization overlaps between the HVBP, HACRP, and HRRP programs. Regression analysis reveal hospitals admitting more minority patients or housing a transplant center are less likely to enroll in any of the three programs. A higher volume of African American patients significantly correlates with worse HACRP and HRRP outcomes. More Hispanic and Asian or Pacific Islander patients also associate with higher HRRP excess readmission rates. Teaching hospitals significantly score worse in the HVBP, HACRP, and HRRP programs. Transplant centers are more likely to report reduced/expected readmissions rates once participating in the HRRP. These findings indicate that while improvements to the policies' evaluation methodologies are necessary, additional research is paramount to precisely determine the required changes
Foreign Facilitators of Weapons Fueling Sudan’s Civil War
Get PDFThis report summarizes evidence of the UAE and Iran facilitating weapons to both sides of the civil war in Sudan.Produced with the support of the Bureau of Conflict and Stabilization Operations, United States Department of State
Maijuna Perspectives on the Establishment and Co-Management of the Maijuna-Kichwa Regional Conservation Area (MKRCA)
Get PDFGrowing awareness of the critical role Indigenous People and Local Communities (IPLC) play in biodiversity conservation has underscored the need to shift conservation practices towards empowering IPLCs, supporting their land rights, traditional practices, and facilitating their political involvement. Despite IPLCs governing over 32% of global land, historically, these communities have faced systemic marginalization and violence in the name of conservation. In response, international calls to action and policies have aimed to enhance IPLC participation in environmental governance through mechanisms like co-management. Adaptive Co-management (ACM) emerges as a promising approach, combining adaptive management’s flexibility with co-management’s collaborative principles. This study evaluates the ACM framework within the Maijuna-Kichwa Regional Conservation Area (MKRCA) in the Peruvian Amazon, established to protect the ancestral lands and biocultural resources of the Maijuna and Kichwa communities. Using Community-Based Participatory Research (CBPR) and appreciative action inquiry, we conducted interviews with 36 community members to assess their perspectives on the MKRCA’s co-management. Findings reveal significant improvements in resource abundance and community safety but also highlight issues with governmental support, communication, and equitable participation. The study emphasizes the need for continuous evaluation and enhanced stakeholder engagement to optimize ACM’s effectiveness, offering culturally responsive recommendations to strengthen the MKRCA’s management and achieve its conservation objectives