Master Degree in Public Policy (Mundus MAPP). Master Thesis
http://hdl.handle.net/10230/43642
2024-03-29T10:44:34ZInnovative claims-making and media diffusion: the case of Mexico City’s anti-monuments
http://hdl.handle.net/10230/52655
Innovative claims-making and media diffusion: the case of Mexico City’s anti-monuments
Burger, Lona Marie Lauridsen
Since 2015, seven large metal structures, termed ‘anti-monuments’ (antimonumentos), have been placed by various civil society actors in Mexico City. Each commemorates a distinct grievance linked to claims of state culpability or inaction. They are atypical of other protest tactics in that they have largely been tolerated by public actors and, seemingly, celebrated by the news media. This study aims to determine the extent to which media coverage has reproduced their claims and contributed to their perceived legitimacy. To answer these questions, the research is theoretically informed by literature from critical policy analysis and social movement studies. While the case study uses a variety of methods, the core empirical analysis relies on qualitative content analysis of 72 articles published by Mexican newspapers since the emergence of the phenomenon (2015-2020). The findings are divided into three principal categories: visibility of claims, resonance, and legitimacy. The results indicate that, in contrast to coverage of other protest actions, the anti-monuments have been profoundly successful in diffusing their claims and gaining favourable media coverage. Although the literature suggests that the media is unlikely to favour the transmission of collective action frames, this tactical innovation was able to unlock a formula for effective media uptake. The text concludes with a discussion of plausible explanatory factors and the broader social and policy implications of these observations. This research is of relevance beyond the case itself and could serve to inform theorization on the relationship between contested claims-making and media diffusion.
Treball fi de màster de: Erasmus Mundus Master’s in Public Policy. Curs 2019-2020
2020-01-01T00:00:00ZWhy do countries develop harm reduction programs?: a mixed methods approach
http://hdl.handle.net/10230/52652
Why do countries develop harm reduction programs?: a mixed methods approach
Brozdowski, Jonathan
This thesis investigates why and how countries develop drug harm reduction programs today. Though they began as a controversial set of ideas challenging global drug policy’s dominant interdiction model, they have evolved over decades of mobilization around HIV and become a global social policy in many ways spearheaded by international organizations in its “medicalized” form. Drawing on understanding of complex multilateralism and the Advocacy Coalition Framework, this thesis uses an ordered probit
regression analysis and a structured focused comparison of two countries to investigate harm reduction’s development. Based on the dataset by Harm Reduction International (HRI) noting the presence or absence of seven programs in 165 countries, analysis found measures of participatory and egalitarian governance to be especially important. Kenya and Cameroon were chosen for case study through Mill's Method of Difference, as a deviant, successful case and as a typical, unsuccessful case, with substantial similarity in important factors except for the chosen variable of interest: civil society participation in government. It concludes that international involvement, civil society mobilization, and government cooperation with CSOs are especially important to harm reduction’s development. In addition to being the first quantitative study and the first comparative case study on the specific factors that lead a country to develop harm reduction programs, this paper offers insight into global governance by showing how a global social policy can transcend national laws and be in some ways implemented by international actors.
Treball fi de màster de: Erasmus Mundus Master’s in Public Policy. Curs 2019-2020
2020-01-01T00:00:00ZExploring persistent policy practices: Germany’s dispersal policy and the accommodation of asylum seekers
http://hdl.handle.net/10230/52619
Exploring persistent policy practices: Germany’s dispersal policy and the accommodation of asylum seekers
Pascucci, Hannah
This dissertation analyzes the apparent tension between the German dispersal policy practice and the allocation and accommodation of asylum seekers. Within the context of the 2015 ‘summer of welcome’, Germany received the highest number of asylum applications not only in its own history, but also in European history. Consequently, it is facing the challenge of accommodating and integrating more than 1.2 million asylum seekers in the coming years. While the practice of dispersal of asylum seekers is based on the Königsteiner Key in line with the discourse of fair and equal distribution and therefore sharing the social and economic burden caused by the cost of accommodation and integration, there seems to be a tension when regarding the limited possibility of providing adequate housing and accommodation. Drawing from experience and fieldwork in Freiburg, a mid-size city with an overwhelmingly green and progressive political orientation in the German federal state of Baden- Württemberg, this thesis, using the Neo-Gramscian understanding of common sense à la Bruff, demonstrates how a persistent common sense logic on equally dispersing asylum seekers is creating tension with the need of accommodating them. By analyzing how and why historically synthesized common sense rooted with the practice of dispersion is locally sedimented and manifest in the accommodation of asylum seekers in Freiburg, this analysis provides a critical understanding of the systemic relationality between dispersal and accommodation and the tension created.
Treball fi de màster de: Erasmus Mundus Master’s in Public Policy. Curs 2018-2019
2019-01-01T00:00:00ZMachine learning for public policy making : how to use data-driven predictive modeling for the social good
http://hdl.handle.net/10230/43488
Machine learning for public policy making : how to use data-driven predictive modeling for the social good
Steuer, Fabian
Machine learning gives computers the ability to learn from data without being explicitly programmed. Due to its excellent prediction abilities, it has recently gained traction in economics, statistics and social sciences. Real-world problems machine learning has been applied to include predicting the probability that individuals commit crimes, targeting hygiene inspections by data-mining online restaurant reviews or estimating poverty levels based on satellite imagery. In this thesis I explore how machine learning can help to solve such and other prediction problems in public policy making and what challenges it faces. My goal is to bring the two fields closer together as most public policy makers likely do not even know that they face prediction problems that machine learning can help solving. After an introduction to prediction problems, I give an overview of how machine learning works and explain under what circumstances machine learning can be used for data-driven predictive modeling for the social good. A case study about predicting hygiene violations in restaurants illustrates the lessons learned and allows to get an idea of what applying machine learning looks like in practice. I then look into the challenges and limitations that machine predictions face in public policy making. Besides the fundamental limits of prediction, these range from technical and human challenges to ethical and legal issues due to biased predictions, black-box algorithms and questions of responsibility.
Treball fi de màster de: Erasmus Mundus Master’s in Public Policy. Curs 2017-2018
2018-01-01T00:00:00Z