2 resultados para academic policy

em Duke University


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\abstract

This dissertation seeks to explain the role of governmental and non-governmental actors in increasing/reducing the emergence of intergroup conflict after war, when group differences have been a salient aspect of group mobilization. This question emerges from several interrelated branches of scholarship on self-enforcing institutions and power-sharing arrangements, group fragmentation and demographic change, collective mobilization for collectively-targeted violence, and conflict termination and the post-conflict quality of peace. This question is investigated through quantitative analyses performed at the sub-national, national, and cross-national level on the effect of elite competition on the likelihood of violence committed on the basis of group difference after war. These quantitative analyses are each accompanied by qualitative, case study analyses drawn from the American Reconstruction South, Iraq, and Cote d'Ivoire that illustrate and clarify the mechanisms evaluated through quantitative analysis.

Shared findings suggest the correlation of reduced political competition with the increased likelihood of violence committed on the basis of group difference. Separate findings shed light on how covariates related to control over rent extraction and armed forces, decentralization, and citizenship can lead to a reduction in violence. However, these same quantitative analyses and case study analysis suggest that the control of the state can be perceived as a threat after the end of conflict. Further, together these findings suggest the political nature of violence committed on the basis of group difference as opposed to ethnic identity or resource scarcity alone.

Together, these combined analyses shed light on how and why political identities are formed and mobilized for the purpose of committing political violence after war. In this sense, they shed light on the factors that constrain post-conflict violence in deeply divided societies, and contribute to relevant academic, policy, and normative questions.

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Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?

The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.

The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.

The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.