Violence against Women Policy and Practice in Uganda - Promoting Justice within the Context of Patriarchy

The goal of this study was to understand how and whether policy and practice relating to violence against women in Uganda, especially Uganda’s Domestic Violence Act of 2010, have had an effect on women’s beliefs and practices, as well as on support and justice for women who experience abuse by their male partners. Research used multi-sited ethnography at transnational, national, and local levels to understand the context that affects what policies are developed, how they are implemented, and how, and whether, women benefit from these. Ethnography within a local community situated global and national dynamics within the lives of women. Women who experience VAW within their intimate partnerships in Uganda confront a political economy that undermines their access to justice, even as a women’s rights agenda is working to develop and implement laws, policies, and interventions that promote gender equality and women’s empowerment. This dissertation provides insights into the daily struggles of women who try to utilize policy that challenges duty bearers, in part because it is a new law, but also because it conflicts with the structural patriarchy that is engrained in Ugandan society. Two explanatory models were developed. One explains factors relating to a woman’s decision to seek support or to report domestic violence. The second explains why women do and do not report DV. Among the findings is that a woman is most likely to report abuse under the following circumstances: 1) her own, or her children’s survival (physical or economic) is severely threatened; 2) she experiences severe physical abuse; or, 3) she needs financial support for her children. Research highlights three supportive factors for women who persist in reporting DV. These are: 1) the presence of an “advocate” or support 2) belief that reporting will be helpful; and, 3) lack of interest in returning to the relationship. This dissertation speaks to the role that anthropologists can play in a multi-disciplinary approach to a complex issue. This role is understanding – deeply and holistically; and, articulating knowledge generated locally that provides connections between what happens at global, national and local levels.

Development Begins at Home: Women and the Domestic Economy in Brazil, 1945-1975

A number of historians of twentieth-century Latin America have identified ways that national labor laws, civil codes, social welfare programs, and business practices contributed to a gendered division of society that subordinated women to men in national economic development, household management, and familial relations. Few scholars, however, have critically explored women's roles as consumers and housewives in these intertwined realms. This work examines the Brazilian case after the Second World War, arguing that economic policies and business practices associated with “developmentalism” [Portuguese: desenvolvimentismo] created openings for women to engage in debates about national progress and transnational standards of modernity. While acknowledging that an asymmetry of gender relations persisted, the study demonstrates that urban women expanded their agency in this period, especially over areas of economic and family life deemed "domestic." This dissertation examines periodicals, consumer research statistics, public opinion surveys, personal interviews, corporate archives, the archives of key women’s organizations, and government officials’ records to identify the role that women and household economies played in Brazilian developmentalism between 1945 and 1975. Its principal argument is that business and political elites attempted to define gender roles for adult urban women as housewives and mothers, linking their management of the household to familial well-being and national modernization. In turn, Brazilian women deployed these idealized roles in public to advance their own economic interests, especially in the management of household finances and consumption, as well as to expand legal rights for married women, and increase women’s participation in the workforce. As the market for women's labor expanded with continued industrialization, these efforts defined a more active role for women in the economy and in debates about the trajectory of national development policies.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

EXPLICIT WRITTEN CORRECTIVE FEEDBACK AND LANGUAGE APTITUDE IN SLA: IMPLICATIONS FOR IMPROVEMENT OF LINGUISTIC ACCURACY

Most second language researchers agree that there is a role for corrective feedback in second language writing classes. However, many unanswered questions remain concerning which linguistic features to target and the type and amount of feedback to offer. This study examined two new pieces of writing by 151 learners of English as a Second Language (ESL), in order to investigate the effect of direct and metalinguistic written feedback on errors with the simple past tense, the present perfect tense, dropped pronouns, and pronominal duplication. This inquiry also considered the extent to which learner differences in language-analytic ability (LAA), as measured by the LLAMA F, mediated the effects of these two types of explicit written corrective feedback. Learners in the feedback groups were provided with corrective feedback on two essays, after which learners in all three groups completed two additional writing tasks to determine whether or not the provision of corrective feedback led to greater gains in accuracy compared to no feedback. Both treatment groups, direct and metalinguistic, performed better than the comparison group on new pieces of writing immediately following the treatment sessions, yet direct feedback was more durable than metalinguistic feedback for one structure, the simple past tense. Participants with greater LAA proved more likely to achieve gains in the direct feedback group than in the metalinguistic group, whereas learners with lower LAA benefited more from metalinguistic feedback. Overall, the findings of the present study confirm the results of prior studies that have found a positive role for written corrective feedback in instructed second language acquisition.