The purpose of this study was to analyze the influences such as age at current offense, gender, race, previous convictions, and the seriousness of crimes that contributed to the decisions received by juveniles in adult court. This study examined a secondary data set from the United States Department of Justice entitled Juvenile Defendants in Criminal Courts (JDCC): Survey of 40 Counties in the United States, 1998. The cases from these 40 jurisdictions represented all filings during one month in 75 of the most populous counties. The current study found significant differences among race, prior criminal history, current offense severity, and juveniles adjudicated in adult court. Future research should therefore continue to examine the impact of juveniles adjudicated in adult court to better inform the debate surrounding the potential dangers associated with juvenile offending and adult criminal sanctions.

An online survey was completed by 40 adolescents and young adults (ages 12 to 22) with physical disabilities for the purpose of exploring their social experiences. The survey focuses on key variables associated with individual identity, group identity, social relationships and activities, and future aspirations. Positive outcome variables were explored including: self-esteem, self-efficacy, body satisfaction, cultural identity, relationship quality, activity participation, and future orientation. Independent variables included sex, population size, ability level, and proximity to disability. Ability level proved to be the most predictive of positive outcomes. Sex, population size, and proximity to disability exhibited small associations to some of the outcome variables.

This thesis examined police practices of the North Carolina Highway Patrol concerning the occurrence of racial profiling. The sample data consisted of motorists stopped in North Carolina by the Highway Patrol between January 1, 2000 and July 31, 2000 (N = 332, 861). The findings suggested that race was a likely factor in pretextual stops. The results also indicate that racial profiling was occurring more in the western region than the eastern region of North Carolina. Theoretical reasons are offered in support of these findings.

This research analyzes cyclists' route decision by considering attributes of road safety and security from GPS records of a mobile application in Bogotá. The dataset comprises 3016 georeferenced routes of cyclists registered in the Biko mobile application during February 2018. This database was complemented with accident and thefts records from public entities, a descriptive statistical univariate analysis (RStudio), a Multiple Correspondence Analysis -MCA- (Stata), with multivariate statistical approach, and geographic component (QGIS and ArcGIS). The methods allowed obtaining: [i] a procedure for characterizing quantitative variables per km of route traveled; [ii] Categorization of continuous variables for establishing multivariate relationships through MCA -prerequisite for using this method instead of using surveys (Mobility survey 2019 cyclists' section in Bogotá); [iii] cyclists' commuting patterns with identification of main Origin - Destination zones (UTAM in Bogotá), and [iv] possible initial conditions for the public policies approach in Bogotá, with a continuous comparison between case studies in: Colombia, Latin America, Europe, and the United States, in order to be replicable for any city. ... [From: Introduction]

Research on attitudes towards immigration policies typically considers the economic and cultural threats that compel many Americans to favor exclusionary policies that curb immigration. Less is understood about how indifference to inequality shapes Americans’ attitudes towards immigration policies—that is, how ‘not caring’ about the unequal conditions faced by immigrants likely has detrimental consequences for their safety and wellbeing. The present research examines indifference to inequality as a predictor for policies that impact opportunities for immigrants to come to the U.S., and who are otherwise undocumented and/or at great risk for exploitation. Using survey data from the American National Election Studies gathered in 2016 (Study 1; n = 3,187) and 2020 (Study 2; n = 6,941), we find that greater indifference to inequality is associated with less support for providing a path to citizenship for undocumented immigrants, and greater support for building a wall between the U.S. and Mexico, independently of other explanatory intergroup variables (e.g., prejudice, threat, and demographic characteristics). However, these associations tend to be moderated by ethnoracial background, such that although indifference to inequality predicts immigration policy attitudes among U.S.-born White Americans, it is not predictive of attitudes among U.S.-born Latino and Black Americans. Furthermore, these associations are not moderated by recent family history of immigration, suggesting that respondents’ group status in the U.S. ethnoracial hierarchy, and not the personal relevance of immigration, may well be driving these associations.

This essay will analyze a selection of characters from Charles Dickens’ novel Hard Times. Characterizations will be analyzed by using a Marxist theoretical framework, e.g., characters’ relations to Marxist concepts such as class struggle, alienation, and stratification will be studied. The purpose of this essay is to use Marxist concepts in order to understand Dickens’ satire and critique of capitalism. This is done by applying a theory criticizing capitalism, namely Marxist theory, to some of the novel’s characters and analyzing these characters according to their relations to the main features of Marxist theory. A few characters are selected for analysis, to distinguish characteristics or traits that satirize society. Moreover, the essay will investigate whether the author alludes to Marxist concepts when satirizing contemporary society. The characters portrayed in the novel are all exposed to a society characterized by hardship, inequality, and class struggle. These concepts are all features of a society that Marxism critiques. Accordingly, the thesis is that Marxist concepts are implicit in the text and do play a role in Dickens’ satirizing of his contemporary, capitalist, industrialized society, despitenot being mentioned explicitly.

The study of household wealth as a distinct dimension of social stratification is crucial to understand the main factors and mechanisms driving the intergenerational reproduction of inequalities in contemporary societies. This dissertation aims to contribute to the expanding literature on wealth inequalities by investigating the role played by parental wealth in shaping children’s educational opportunities. More specifically, the dissertation concentrates on three main research axes. First, it investigates the relevance of wealth gaps in education in Italy, a country that received little attention in the literature so far. In doing so, it evaluates wealth gaps in the attainment of upper secondary degrees, in the enrolment at university, and in the attainment of tertiary degrees. Second, it aims to clarify the various roles played by different levels and components of parental wealth in providing children with advantages in educational outcomes. In this regard, it provides a theoretical reflection linking potential wealth mechanisms to the combination of levels and components of wealth and it empirically evaluates their relevance in the transition to post-secondary education. Third, it explores whether and how wealth gaps in education vary across different national contexts. Broad international comparisons are still missing in the literature and single-country studies are hardly comparable. To this purpose, this dissertation aims at evaluating wealth gaps in the access to post-secondary education across 14 European countries while also accounting for the relative importance of different wealth components.

This work aims at developing a novel machine learning method to investigate heterogeneity in neurodevelopmental disorders, with a focus on autism spectrum conditions (ASCs). In ASCs, heterogeneity is shown at several levels of analysis, e.g., genetic, behavioral, throughout developmental trajectories, which hinders the development of effective treatments and the identification of biological pathways involved in gene-cognition-behavior links. ASC diagnosis comes from behavioral observations, which determine the cohort composition of studies in every scientific field (e.g., psychology, neuroscience, genetics). Thus, uncovering behavioral subtypes can provide stratified ASC cohorts that are more representative of the true population. Ideally, behavioral stratification can (1) help to revise and shorten the diagnostic process highlighting the characteristics that best identify heterogeneity; (2) help to develop personalized treatments based on their effectiveness for subgroups of subjects; (3) investigate how the longitudinal course of the condition might differ (e.g., divergent/convergent developmental trajectories); (4) contribute to the identification of genetic variants that may be overlooked in case-control studies; and (5) identify possible disrupted neuronal activity in the brain (e.g., excitatory/inhibitory mechanisms). The characterization of the temporal aspects of heterogeneous manifestations based on their multi-dimensional features is thus the key to identify the etiology of such disorders and establish personalized treatments. Features include trajectories described by a multi-modal combination of electronic health records (EHRs), cognitive functioning and adaptive behavior indicators. This thesis contributes in particular to a data-driven discovery of clinical and behavioral trajectories of individuals with complex disorders and ASCs. Machine learning techniques, such as deep learning and word embedding, that proved successful for e.g., natural language processing and image classification, are gaining ground in healthcare research for precision medicine. Here, we leverage these methods to investigate the feasibility of learning data-driven pathways that have been difficult to identify in the clinical practice to help disentangle the complexity of conditions whose etiology is still unknown. In Chapter 1, we present a new computational method, based on deep learning, to stratify patients with complex disorders; we demonstrate the method on multiple myeloma, Alzheimer’s disease, and Parkinson’s disease, among others. We use clinical records from a heterogeneous patient cohort (i.e., multiple disease dataset) of 1.6M temporally-ordered EHR sequences from the Mount Sinai health system’s data warehouse to learn unsupervised patient representations. These representations are then leveraged to identify subgroups within complex condition cohorts via hierarchical clustering. We investigate the enrichment of terms that code for comorbidities, medications, laboratory tests and procedures, to clinically validate our results. A data analysis protocol is developed in Chapter 2 that produces behavioral embeddings from observational measurements to represent subjects with ASCs in a latent space able to capture multiple levels of assessment (i.e., multiple tests) and the temporal pattern of behavioral-cognitive profiles. The computational framework includes clustering algorithms and state-of-the-art word and text representation methods originally developed for natural language processing. The aim is to detect subgroups within ASC cohorts towards the identification of possible subtypes based on behavioral, cognitive, and functioning aspects. The protocol is applied to ASC behavioral data of 204 children and adolescents referred to the Laboratory of Observation Diagnosis and Education (ODFLab) at the University of Trento. In Chapter 3 we develop a case study for ASCs. From the learned representations of Chapter 1, we select 1,439 individuals with ASCs and investigate whether such representations generalize well to any disorder. Specifically, we identify three subgroups within individuals with ASCs that are further clinically validated to detect clinical profiles based on different term enrichment that can inform comorbidities, therapeutic treatments, medication side effects, and screening policies. This work has been developed in partnership with ODFLab (University of Trento) and the Predictive Models for Biomedicine and Environment unit at FBK. The study reported in Chapter 1 has been conducted at the Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai (NY).

Educational credentials have a central role in contemporary societies. However, social origins continue to affect educational performances and transitions well before children enter compulsory school, thus threatening future outcomes and development. By interacting research streams from economics, psychology, and pedagogy, this dissertation locates within the literature on child development, early education, and social stratification, and it aims at further contributing to the sociological evidence on the mechanisms that lead to inequalities in skills. The core of this dissertation lies in the analysis of the characteristics of the early childhood educational system (ECE) and of the home learning environment (HLE), as growth-promoting or unfavourable contexts for the development of both cognitive and noncognitive skills. Adopting recent cross-national and longitudinal data, this dissertation asks (1) whether and how much ECE matters in the lives of children around Europe, leaving long-lasting traces on their achievements once adolescents; (2) whether and how much parental social position, beliefs, and other family and child characteristics play a role in the care selection process in Germany; and (3) whether and how much quality in HLE and ECE contributes at explaining differences in skills before entering primary school in Ireland.

Research indicates that African American women breastfeed at the lowest rates of any racial/ethnic group in the U.S. Breastfeeding has shown to have numerous health benefits for both mother and baby, making the lower rates of breastfeeding among African Americans a public health concern. Racial disparities in healthcare may contribute to these discrepancies. This research will analyze the perceptions of information and social support for breastfeeding provided by healthcare providers among a sample of African American mothers who breastfed their babies. The study asks: Do participants regard their healthcare providers as supportive of breastfeeding? Data were collected through in-depth qualitative interviews with 22 African American mothers. Participants interpreted their providers’ opinions on breastfeeding and formula and discussed whether they felt supported to breastfeed. Findings reveal which healthcare providers were perceived to be the most supportive of breastfeeding and themes within the time-frame codes: pregnancy, labor and birth, immediately after birth, and postpartum. The majority of participants felt supported during the first three stages. However, during the postpartum period, there was a lack of assistance from healthcare providers, resulting in limited breastfeeding support. Participants that did receive postpartum support typically received verbal affirmation, rather than given useful information.