This work analyzes the ways in which transnational migration transforms and is transformed by the sexual dimensions of identity. It presents the experience of Colombian homosexual men who have migrated to New York City in the last twenty-five years as a case that illuminates identity transformations in the process of transnational migration. Throughout ethnographic research, this work finds that immigration greatly impacts family arrangements and patterns of inclusion and exclusion in the immigrant's original and recipient society.

Comment gouverne-t-on les pauvres aujourd'hui ? Sur la base d'une enquête comparative et qualitative entre un dispositif français (le Revenu de solidarité active) et un programme new-yorkais (Opportunity NYC), cette thèse propose d'analyser les mécanismes de la lutte contre la pauvreté reposant sur la conditionnalité comportementale. Porter l'indigence à l'agenda, justement par qu'elle est éminemment politique, fait l'objet d'un important travail de dépolitisation. Importation de modèles de l'étranger, construction de données chiffrées, mobilisation d'un protocole expérimental : tels sont les principaux procédés d'objectivation et de légitimation mobilisés. La teneur de l'aide apportée aux pauvres change. Se démarquant d'une logique simplement économique qui voudrait que la récompense monétaire engendre un comportement prédéfini, et desserrant une méthode strictement punitive où la sanction tient lieu de formule de commandement, ces politiques sociales conditionnelles renouvellent les termes des relations entre populations pauvres et pouvoir politique. Les techniques de gouvernement mises en œuvre s'inspirent du « nudge », c'est-à-dire d'une forme paradoxale d'autonomie institutionnalisée où les bons comportements tiennent lieu d'échappatoire à la pauvreté. L'aide sociale comme relevant du droit ou du statut est dépassée. Sa légitimité ne se fonde plus sur l'efficacité, dans la veine du new public management, mais sur un sens commun réformateur ayant le mérite pour objet. Les clivages partisans paraissent s'affaisser autour de cette forme de gouvernement moral. L'éthique du travail et la « bonne volonté » des pauvres sont mises en scène pour justifier l'octroi de subsides sociaux. L'expérimentation sociale, passée au prisme du jeu politique, confère un caractère « scientifique » et « objectif » à ces présupposés. Au final, c'est à un gouvernement de la pauvreté fait de politique et d'usages de la scientificité que ce travail est consacré. / This study provides a comparative analysis of two anti-poverty initiatives in New York (Opportunity NYC) and in France (Revenue for Active Solidarity). It is based on extensive fieldwork and numerous interviews in both countries. Conditioning subsidies on behavior and giving a nudge “to do the right thing” by structuring the choices of the poor now defines the way poverty is fought against. The political agenda is built around the concept of making conditional social policy non-partisan solutions. Policy transfers, data-making or experimental design figure among the ways of taking politics out of policy-making. This study finds there is no such thing and that these programs reflect a substantive political orientation. The mechanisms used to motivate the poor are different. They are more than just an economic incentive that binds reward and behavior together. They are less than a coercive approach where work is mandatory to get relief. The nudge emerges as a third way that consists in a paradoxical injunction: governing the poor to govern themselves. No entitlement there. If this is nothing new in America, this is new in France. Hence, policy is not legitimated by effectiveness, like it used to be with new public management, but by moralistic assumptions. Reform is based more on commonsense than technical arguments. The work ethics and the “good choices” justify the money spent for the poor (public in France, private in New York). The experimental design, once appropriated by political instances, gives a scientific glow to these programs so they appear “universal” and “neutral”. In a nutshell, this dissertation explores how poverty is governed by a policy-mix of politics and science

Masters in Public Administration - MPA / The buzzword in recent development policy-making is PM&E. The notion is that; participatory approaches have the appropriate remedy to curb the problem of exclusion. That notwithstanding, the approach has become rhetoric in certain quarters rather than practical as admitted by some scholars who hold the principle in high esteem. Inferences from the concept of empowerment as put forward by Narayan (2005), might be the premise for rethinking the debate surrounding the inclusion of beneficiaries in participatory monitoring and evaluation because of its undulation positive effect on project outcomes. Over the past two decades, Ghana has initiated and implemented a good number of national policies and strategies that are youth-centered. The majority of these youth programmes are usually delineated in most public policies on thematic areas such as employment, education, health, among many others. A total of 120 respondents (project beneficiaries) were randomly selected for questionnaire administration, and 1 in-depth interviewed was conducted for this study. Using a Participation Perception Index (PPI), developed to assess the youth perception of the extent to which they were involved in the PM&E, the following were ascertained. It was evident that the youth were only made to actively participate in the data collection (as respondents) process. Evidently, the primary objective of the implementing agency was to secure the youth (beneficiaries) job rather than involving them in the project PM&E. The qualitative analysis also highlighted other critical factors affecting both the implementing agency and the youth (skills or know-how, cost, lack of beneficiaries' interest, non-existence of beneficiaries' associations) to ensure active participation. The study concluded that the end goal of the youth intervention programme is tied into the ideas of project sustainability which can be achieved when the various stakeholders are all on board in the PM&E.

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Cities have adopted evolving urban digitization strategies, and most of those increasingly focus on data, especially in the field of public transportation. Transportation data have intuitively spatial and temporal characteristics, for they are often described with when and where the trips occur. Since a trip is often described with many attributes, the transportation data can be presented with a tensor, a container which can house data in $N$-dimensions. Unlike a traditional data frame, which only has column variables, tensor is intuitively more straightforward to explore spatio-temporal data-sets, which makes those attributes more easily interpreted. However, it requires unique techniques to extract useful and relatively correct information in attributes highly correlated with each other. This work presents a mixed model consisting of tensor decomposition combined with seasonal vector autoregression in time to find latent patterns within historical taxi data classified by types of taxis, pick-up and drop-off times of services in NYC, so that it can help predict the place and time where taxis are demanded. We validated the proposed approach using the experiment evaluation with real NYC tax data. The proposed method shows the best prediction among alternative models without geographical inference, and captures the daily patterns of taxi demands for business and entertainment needs.

This thesis investigates the intonation of Argentine Spanish in Queens, NYC, with the goal of verifying if the unique prosody of producing early peak alignments in the F0 of Argentine Spanish, specifically of Porteños (those from Buenos Aires), is maintained among the intense contact influences with other varieties of Spanish in the area. Previous studies have reported this early peak alignment phenomenon in the Spanish spoken in Buenos Aires, and this paper strives to see if this still occurs among Argentine Spanish speakers in New York City. The Buenos Aires speakers were compared with other native Argentine Spanish speakers in New York City who originated from provinces other than Buenos Aires (primarily from Mendoza) to verify if the dialectal varieties of Argentine Spanish had remained the same under the intense language contact situation of living in Queens. The data in the current study are from interviews recorded during the summer of 2014 in the Queens, NYC neighborhood of Elmhurst. Acoustic information obtained includes total syllable duration, F0 measurements, and F0 patterns. Additional linguistic variables included vowel type and vowel syllable position within a word. Extralinguistic variables included speaker sex, age, origin in Argentina, educational level, number of years in NYC, and number of years in Argentina. Results indicate that early peak alignment does indeed occur among Argentine speakers in Queens, NYC; however, it is interesting to note that it not only occurs in the informants from Buenos Aires as predicted, but in the informants from outside Buenos Aires as well. This suggests that the Outside Buenos Aires speakers are undergoing prosodic dialectal leveling with their pitch accent patterns and an increase in stressed syllable duration as occur naturally among the Buenos Aires speakers.

Many metropolitan cities motivate people to exploit public bike-sharing programs as alternative transportation for many reasons. Due to its’ popularity, multiple types of research on optimizing public bike-sharing systems is conducted on city-level, neighborhood-level, station-level, or user-level to predict the public bike demand. Previously, the research on the public bike demand prediction primarily focused on discovering a relationship with weather as an external factor that possibly impacted the bike usage or analyzing the bike user trend in one aspect. This work hypothesizes two external factors that are likely to affect public bike demand: weather and air pollution. This study uses a public bike data set, daily temperature, precipitation data, and air condition data to discover the trend of bike usage using multiple machine learning techniques such as Decision Tree, Naïve Bayes, and Random Forest. After conducting the research, each algorithm’s output is evaluated with performance comparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is an efficient classifier for the bike demand prediction by weather and precipitation, and Decision Tree performs best for the bike demand prediction by air pollutants. Also, the three class labelings in the daily bike demand has high specificity, and is easy to trace the trend of the public bike system.