15064 research outputs found
Sort by
When Professionalization Is Not Enough: Teachers and National Board Certification
No full textThis historical and qualitative study focuses on understanding how National Board Certification (NBC) for teachers can “professionalize” teachers. Because NBC meant different things to different parties, the conditions that led up to the development of NBC and the motivations of various players in creating and promoting it are detailed. The conditions are explained through brief histories on: the position of teachers since the advent of the American public education system in the 19th century; criticism against the education establishment that intensified with the launch of Sputnik; Sputnik as the inspiration behind A Nation at Risk, the federal report that blamed teachers for the “rising tide of mediocrity,” in U.S. schools; the Carnegie Corporation’s response, A Nation Prepared: Teachers for the 21st Century, which recommended a national board for professional teaching standards that would create a certification, NBC, that would improve teacher quality by professionalizing them; the professionalization of doctors as a model for the professionalization of teachers; and finally, the experience of NBC in Chicago. In addition, the development and content of NBC are described in detail to explain how NBC demonstrated the sophistication of teaching. Because the project focused on the professionalization of teachers, 145 teachers in four schools in Chicago with large proportions of Nation Board certified Teachers (NBCTs) were interviewed. The schools included one on probation, one that was high-performing, and two that were high ranking schools. NBCTs and non-NBCTs were asked about their challenges, views of teacher quality, professionalization, and NBC. Their experiences were analyzed through the concept of legitimacy, self-determination theory, the creation of collective identity defining stories, uncertainty in teaching, favoritism, and a theory of power in schools. An additional ten interviews were conducted with administrators and other players in education. The interviews at the four schools show that NBC would not allow teachers to play a more active role in improving the school on probation, and it did not cause the teachers to attribute their success to NBC in the highest-performing school. In the remaining two schools, NBC would exacerbate divisions and conflicts among faculty and the principal to the point where NBC was no longer spoken about or promoted in order to reduce conflict. These results demonstrate that legitimacy is not a precursor to professional status and that it is not enough to make a new professional credential meaningful to those outside of a profession without considering the self-interest of the profession. This approach led to a lack of interest in NBC by both outside parties and teachers. Parties outside of education were motivated to develop NBC to “professionalize” teachers in ways that conform to sociological and lay understandings of what it means to be professionals without understanding that teachers already consider themselves professional in ways that run counter to those understandings. Theirs is a collaborative professionalism. Thus, NBC should do more than identify high performing teachers, in isolation. To be relevant to teachers, NBC should instead focus on building professional cultures of autonomous and effective teachers at the school level
Operational Decisions in Platform Competition with Network Externalities
No full textThe structure of network externalities influences platform competition and can determine whether a two-sided market is winner-takes-all or highly contestable. Yet, because of analytical challenges, relatively little is known about these structures in different markets. This thesis investigates how the structure of externalities impacts the performance of platforms and competitive outcomes. In the first chapter, we propose a model of platform competition in ride-hailing, identifying the structure of externalities that arise from congestion. After finding representations of congestion, which serve as micro-foundations for platform competition, we provide general conditions for the existence and uniqueness of equilibrium. Our results demonstrate that ride-hailing platforms benefit from economies of scale, becoming more efficient as they attract more riders and drivers. This could lead to winner-takes-all scenarios in markets with low demand or attractive outside options. However, service variability arising from differing distances between riders and drivers introduces differentiation, enabling multiple platforms to coexist in suitable markets. In such cases, entrants can leverage service variability to enter, potentially driving profits to zero and making the market highly contestable. The results challenge conventional wisdom, suggesting that having a large network may no longer guarantee survival and that platforms need credible commitment mechanisms to retain users. We discuss the reasons behind these observations, shedding light on potential strategies that platform managers can follow while avoiding the ones that can inadvertently harm competition. In the second chapter, we extend the model to investigate the platforms' decisions of using loyalty programs. The platforms can either attract multihoming drivers from a shared pool or offer loyalty programs that incentivize exclusive participation through bonuses and service commitments. We analyze equilibrium outcomes under duopoly, oligopoly, and entry scenarios and identify conditions under which platforms prefer loyalty programs over a pool of drivers. Our findings highlight that loyalty programs can benefit both platforms, riders, and drivers. When there is no entry, they help reduce costs and full prices and benefit riders. When there is entry, they help platforms deter entrants. In both cases, drivers are better-off due to heterogeneity in preferences
NSF Workshop Report: Exploring Measurements and Interpretations of Intelligent Behaviors Across Animal Model Systems
Get PDFDefining intelligence is a challenging and fraught task, but one that neuroscientists are repeatedly confronted with. A central goal of neuroscience is to understand how phenomena like intelligent behaviors emerge from nervous systems. This requires some determination of what defines intelligence and how to measure it. The challenge is multifaceted. For instance, as we begin to describe and understand the brain in increasingly specific physical terms (e.g., anatomy, cell types, activity patterns), we amplify an ever-growing divide in how we connect measurable properties of the brain to less tangible concepts like intelligence. As our appreciation for evolutionary diversity in neuroscience grows, we are further confronted with whether there can be a unifying theory of intelligence. The National Science Foundation (NSF) NeuroNex consortium recently gathered experts from multiple animal model systems to discuss intelligence across species. We summarize here the different perspectives offered by the consortium, with the goal of promoting thought and debate of this ancient question from a modern perspective, and asking whether defining intelligence is a useful exercise in neuroscience or an ill-posed and distracting question. We present data from the vantage points of humans, macaques, ferrets, crows, octopuses, bees, and flies, highlighting some of the noteworthy capabilities of each species within the context of each species’ ecological niche and how these may be challenged by climate change. We also include a remarkable example of convergent evolution between primates and crows in the circuit and molecular basis for working memory in these highly divergent animal species
Spatial Population Genetics of Variants Under Selection in the Context of Two Biomedical Problems
No full textSpatial population genetic models are useful tools to study the impact of geographical processes, such as migration and population structure, on genetic variation in humans and other species. Several research areas in human biomedical genetics are intertwined with spatial processes, particularly when considering variants affected by natural selection. In this dissertation, I will examine how spatial population genetic models can provide insight in two such areas: (i) the spread of adaptive viral lineages in epidemics, and (ii) the identification of deleterious variants associated with disease traits from large genetic samples. First, in Chapter 2, I review a long history of theoretical modeling related to the spread of adaptive alleles in spatial populations, and discuss how such models could be adapted to study variants of concern in SARS-CoV-2 and similar viruses. In Chapters 3 and 4, I turn to the second question of characterizing deleterious variants from genetic samples. In particular, I focus on the role of spatially uneven sampling designs on ascertained allele frequencies of rare, deleterious variants. In Chapter 3, I present a population genetic model for the distribution of carriers of deleterious alleles in a structured population – accounting for dispersal, drift, selection, mutation, and uneven spatial sampling simultaneously – and derive key properties of the site frequency spectrum (SFS) as well as downstream quantities. In Chapter 4, I provide an empirical analysis of how spatially uneven sampling impacts sampled allele frequencies, and compare these results to expectations under the model of Chapter 3 using a likelihood-based framework. In sum, the work presented here demonstrates the utility of spatial population genetics-based approaches for studying problems of biomedical interest, and suggests new approaches for improving such models and their application as the availability of genetic data continues to grow
Clinical Trial Discussion and Participation in a Breast Cancer Cohort by Race and Ethnicity
Get PDFImportance: Racial and ethnic disparities in breast cancer clinical trial participation pose a significant barrier to providing equitable care. Black and Hispanic patients are underrepresented in clinical trials, and an improved understanding of barriers to enrollment is needed. Objective: To examine patterns of clinical trial discussion and participation and patient attitudes toward clinical trial participation in a diverse cohort of patients with breast cancer. Design, Setting, and Participants: This cross-sectional study used survey data from patients enrolled in the Chicago Multiethnic Epidemiologic Breast Cancer Cohort. Patients were queried about clinical trial discussion and subsequent enrollment in a therapeutic clinical trial. Barriers to trial enrollment were also assessed. Surveys were conducted from July to September 2022, and data were analyzed from February to October 2024. Exposure: Self-reported race and ethnicity, including Asian, Black, Hispanic, and White. Main Outcomes and Measures: Outcomes of interest were discussing participation in a breast cancer clinical trial with a health care practitioner, participating in a clinical trial, and barriers to trial enrollment. Results: Of 1150 respondents (mean [SD] age, 53.7 [11.9] years), 51 (4.4%) were Asian, 224 (19.5%) were Black, 35 (3.1%) were Hispanic, and 838 (73.0%) were White. A total of 447 respondents (38.9%) reported discussing trial participation with a health care practitioner. There were no differences in trial discussion between White patients and other racial groups (Asian: adjusted odds ratio [AOR], 0.75; 95% CI, 0.31-1.82; Black: AOR, 1.31; 95% CI, 0.78-2.21; Hispanic: AOR, 0.73; 95% CI, 0.26-2.08). Among 443 patients offered a trial, 285 (64.3%) participated. While there were differences in trial participation across racial and ethnic groups, these differences were not significant after adjusting for sociodemographic and clinical factors. Among 158 patients who did not enroll in the trial offered, 37 (23.4%) reported ineligibility, 17 (10.8%) were worried about the possibility of getting a placebo, 16 (10.1%) were worried about extra time required, and 14 (8.9%) were worried about possible adverse effects. Conclusions and Relevance: This cross-sectional study demonstrated that when offered, patients across racial and ethnic groups were equally likely to participate in clinical trials. In addition to ineligibility, time toxicity was a significant barrier to enrollment. These data provide valuable insights that can serve as a roadmap for how to expand access to trials for all patients, regardless of racial, ethnic, and socioeconomic background.</p
“I Don’t Think Either Side Gets It”: Rural Midwestern Women on Gender, Politics, and Being Left Behind
Get PDFThis thesis investigates how gender and geographic identity intersect to shape political behavior in rural American communities, with a particular focus on rural women. While rural political alignment is often viewed through economic or partisan lenses, this research highlights the deeper cultural, social, and gendered forces at play. Drawing on 12 in-depth interviews with women across the rural Midwest, the study explores how rural consciousness, traditional gender roles, and educational outmigration influence political affiliation and values. Findings reveal stark differences in how liberal and conservative women perceive the role of gender in their lives: liberal women frequently identify gendered limitations and cite reproductive rights and healthcare as key political concerns, while conservative women tend to view traditional gender roles as natural and unproblematic, emphasizing fiscal responsibility and self-reliance. The thesis also demonstrates how rural identity is shaped by feelings of exclusion from national discourse and by misrepresentation in media and politics. Ultimately, this project contributes to a more nuanced understanding of rural voting behavior by centering women’s voices and highlighting the complex relationship between gender, place-based identity, and political polarization in the United States
Seeking Opportunity: The Unspoken Barrier to College Enrollment for First-Generation Low-Income Students
Get PDFThis qualitative research paper aims to understand the role of parental support in determining college enrollment outcomes for high achieving first-generation low-income students from Houston, TX. Although the current literature recognizes the economic, informational, and resource-based barriers to enrolling into a selective out-of-state college, there is a pressing gap in the literature: assessing the effect of parental support. Thus, I breakdown parental support into the following typologies: emotional apprehension, absolute assertiveness, and resource-bound support. I conducted 16 semi-structured interviews with current college students and alumni of EMERGE HISD— a program that offers high achieving low-income students guidance to apply, enroll, and graduate from the nation’s most selective out-of-state universities while minimizing financial burden. Consequently, I identified the following three themes in my findings: the parental shielding effect, the constructive disobedience phenomenon, and the scarcity of resources. Notably, these three themes align with the typologies of parental support. Overall, the lack of parental support often inhibited— and at times, prohibited— first-generation low-income students from enrolling into a selective out-of-state university. Because these schools offer low-income students with the greatest financial aid packages and professional earnings potential, this research aims to decrease barriers to upward mobility in the United States
Generative AI for Bayesian Computation
Get PDFGenerative Bayesian Computation (GBC) provides a simulation-based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to map samples from a base distribution to the posterior distribution. Our method applies equally to parametric and likelihood-free models. By generating a large training dataset of parameter–output pairs inference is recast as a supervised learning problem of non-parametric regression. Generative quantile methods have a number of advantages over traditional approaches such as approximate Bayesian computation (ABC) or GANs. Primarily, quantile architectures are density-free and exploit feature selection using dimensionality reducing summary statistics. To illustrate our methodology, we analyze the classic normal–normal learning model and apply it to two real data problems, modeling traffic speed and building a surrogate model for a satellite drag dataset. We compare our methodology to state-of-the-art approaches. Finally, we conclude with directions for future research
Private Information and Price Regulation in the US Credit Card Market
Get PDFThe 2009 CARD Act limited credit card lenders' ability to raise borrowers' interest rates on the basis of new information. Pricing became less responsive to public and private signals of borrowers' risk and demand characteristics, and price dispersion fell by one-third. I estimate the efficiency and distributional effects of this shift toward more pooled pricing. Prices fell for high-risk and price-inelastic consumers, but prices rose elsewhere in the market and newly exceeded willingness to pay for over 30% of the safest subprime borrowers. On net, average traded prices fell and consumer surplus rose at all credit scores. Higher consumer surplus was partly driven by a fall in lender profits, and partly by the Act's insurance value to borrowers who could retain favorable pricing after adverse changes to their default risk. The relatively high level of pre-CARD-Act markups was crucial for realizing these surplus gains