Local Ownership in Funding mechanisms’ Support to Civil Society Organizations’ Peacebuilding efforts

With the purpose of delivering “clarity through specificity” (Uphoff and Cohen 1980), this thesis explores the vaguely defined concept of local ownership in the area of international funding mechanisms support to “local” civil society’s peacebuilding efforts in post agreement Colombia. It looks at Colombian civil society organizations (CSOs) understanding and perceptions of the principle, its presence in relevant policies, and how different aspects of local ownership are affected by the operational procedures and practice of the three selected funds (or funding mechanisms) for support to civil society (UN Multi-Partner Trust Fund for Sustaining Peace in Colombia (UNMPTF), European Union Trust Fund for Peace in Colombia and FOS – Swedish Norwegian Fund for support to Colombian civil society). Finally, based on CSOs input and the findings in relevant policies and funding mechanisms operational measures, it proposes practical recommendations on how to operationalize the principle in funding mechanisms support to CSOs in response to the main research question. At the core of the problem of local ownership is its lack of a clear definition, or rather a lack of conceptualization in a specific context, and – despite the presence of the concept in a large amount of policy documents – there is a lack of empirical data and research on the topic. This research was done through a case study, gathering information through a survey with informants from 134 diverse Colombian CSOs working with peacebuilding, a desk study of policy documents from related donors’ agencies (NORAD, SIDA and EC DAC) and of operational procedures of the three selected funding mechanisms. The main findings indicate that CSOs find local ownership as important both as a principle for donors work and in the CSOs work, and crucial to success and sustainability. According to the majority, local ownership is about effective participation with reciprocity in the relationships, respect for CSOs autonomy and independence, as well as recognition of their local agency and capacity. Regarding policies, the main findings show that, the concept is highly present throughout policy documents, although not so much through the word “local ownership” exactly, through related key elements that Laclau (1996) calls “chains of equivalence”. Regarding donor mechanisms practice of local ownership, the findings suggest that while the operational iii procedures and practice allow for some aspects of local ownership, there are important obstacles found in each of the three categories established: In category 1, on accessibility and availability the high threshold and difficult procedures impede a variety of CSOs to access in two of three mechanisms. In the 2nd category concerning independence and autonomy, despite the respect CSOs enjoy from donors towards their independence and autonomy, the short term projectization of support, possibly related to donors need for quick and quantifiable results, combined with the lack of capacity building and flexibility, finally restrict the independence and autonomy allowed for. Finally, in the 3rd category on participation/ legitimacy/ accountability there seem to be a lack of implementation of quality participatory approaches and what SIDA, in Guiding Principle (GP) 5, refers to as a check box of donors. Furthermore, according to the CSOs there are important gaps especially the projectization of support and the lack of sustainability this gives, as well as the lack of contextual understanding from the donors, especially on regional questions, giving less relevant donor programs. Finally, the above findings suggest that there is need for a clear intervention logic which should include an analysis to establish clarity on what localness and ownership mean to each donors’ mechanism, a plan for participatory approaches, and specific operational guidelines on the principle for this principle to “trickle down” to practice. It is important to mention that this thesis only included desk studies and surveys as data collection methods, and no qualitative interviews with donors, nor CSOs were carried out, something which could mean there are aspects not considered when drawing the conclusions.Masteroppgave i demokratibyggingSAMPOL650MASV-DEMO

Toward Geo-social Information Systems: Methods and Algorithms

The widespread adoption of GPS-enabled tagging of social media content via smartphones and social media services (e.g., Facebook, Twitter, Foursquare) uncovers a new window into the spatio-temporal activities of hundreds of millions of people. These \footprints" open new possibilities for understanding how people can organize for societal impact and lay the foundation for new crowd-powered geo-social systems. However, there are key challenges to delivering on this promise: the slow adoption of location sharing, the inherent bias in the users that do share location, imbalanced location granularity, respecting location privacy, among many others. With these challenges in mind, this dissertation aims to develop the framework, algorithms, and methods for a new class of geo-social information systems. The dissertation is structured in two main parts: the rst focuses on understanding the capacity of existing footprints; the second demonstrates the potential of new geo-social information systems through two concrete prototypes. First, we investigate the capacity of using these geo-social footprints to build new geo-social information systems. (i): we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. With the help of a classi cation component for automatically identifying words in tweets with a strong local geo-scope, the location estimator places 51% of Twitter users within 100 miles of their actual location. (ii): we investigate a set of 22 million check-ins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. Concretely, we observe that users follow simple reproducible mobility patterns. (iii): we compare a set of 35 million publicly shared check-ins with a set of over 400 million private query logs recorded by a commercial hotel search engine. Although generated by users with fundamentally di erent intentions, we nd common conclusions may be drawn from both data sources, indicating the viability of publicly shared location information to complement (and replace, in some cases), privately held location information. Second, we introduce a couple of prototypes of new geo-social information systems that utilize the collective intelligence from the emerging geo-social footprints. Concretely, we propose an activity-driven search system, and a local expert nding system that both take advantage of the collective intelligence. Speci cally, we study location-based activity patterns revealed through location sharing services and nd that these activity patterns can identify semantically related locations, and help with both unsupervised location clustering, and supervised location categorization with a high con dence. Based on these results, we show how activity-driven semantic organization of locations may be naturally incorporated into location-based web search. In addition, we propose a local expert nding system that identi es top local experts for a topic in a location. Concretely, the system utilizes semantic labels that people label each other, people's locations in current location-based social networks, and can identify top local experts with a high precision. We also observe that the proposed local authority metrics that utilize collective intelligence from expert candidates' core audience (list labelers), signi cantly improve the performance of local experts nding than the more intuitive way that only considers candidates' locations. ii

Tuiteamos o pongamos un tuit? Investigating the Social Constraints of Loanword Integration in Spanish Social Media

Speakers of non-English languages often adopt loanwords from English to express new or unusual concepts. While these loanwords may be borrowed unchanged, speakers may also integrate the words to fit the constraints of their native language, e.g. creating Spanish tuitear from English tweet. Linguists have often considered the process of loanword integration to be more dependent on language-internal constraints, but sociolinguistic constraints such as speaker background remain only qualitatively understood. We investigate the role of social context and speaker background in Spanish speakers\u27 use of integrated loanwords on social media. We find first that newspaper authors use the integrated forms of loanwords and native words more often than social media authors, showing that integration is associated with formal domains. In social media, we find that speaker background and expectations of formality explain loanword and native word integration, such that authors who use more Spanish and who write to a wider audience tend to use integrated verb forms more often. This study shows that loanword integration reflects not only language-internal constraints but also social expectations that vary by conversation and speaker

Do you see what I see? Images of the COVID-19 pandemic through the lens of Google

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the “picture” of the Covid-19 pandemic accessed by users differs. We explore variations in what users “see” concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four regions of Europe, asking them to help us create a photo documentary of Covid-19 by providing image search queries. Analysing the queries, we find five common themes describing information needs. Next, we study three sources of variation - users’ information needs, their geo-locations and query languages - and analyse their influences on the similarity of results. We find that users see the pandemic differently depending on where they live, as evidenced by the 46% similarity across results. When users expressed a given query in different languages, there was no overlap for most of the results. Our analysis suggests that localisation plays a major role in the (dis)similarity of results, and provides evidence of the diverse “picture” of the pandemic seen through Google

Logging Statements Analysis and Automation in Software Systems with Data Mining and Machine Learning Techniques

Log files are widely used to record runtime information of software systems, such as the timestamp of an event, the name or ID of the component that generated the log, and parts of the state of a task execution. The rich information of logs enables system developers (and operators) to monitor the runtime behavior of their systems and further track down system problems in development and production settings. With the ever-increasing scale and complexity of modern computing systems, the volume of logs is rapidly growing. For example, eBay reported that the rate of log generation on their servers is in the order of several petabytes per day in 2018 [17]. Therefore, the traditional way of log analysis that largely relies on manual inspection (e.g., searching for error/warning keywords or grep) has become an inefficient, a labor intensive, error-prone, and outdated task. The growth of the logs has initiated the emergence of automated tools and approaches for log mining and analysis. In parallel, the embedding of logging statements in the source code is a manual and error-prone task, and developers often might forget to add a logging statement in the software's source code. To address the logging challenge, many e orts have aimed to automate logging statements in the source code, and in addition, many tools have been proposed to perform large-scale log le analysis by use of machine learning and data mining techniques. However, the current logging process is yet mostly manual, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and content prediction, i.e., `where and what to log', are of high interest. In addition, approaches that can automatically mine and extract insight from large-scale logs are also well sought after. Thus, in this research, we focus on predicting the log statements, and for this purpose, we perform an experimental study on open-source Java projects. We introduce a log-aware code-clone detection method to predict the location and description of logging statements. Additionally, we incorporate natural language processing (NLP) and deep learning methods to further enhance the performance of the log statements' description prediction. We also introduce deep learning based approaches for automated analysis of software logs. In particular, we analyze execution logs and extract natural language characteristics of logs to enable the application of natural language models for automated log le analysis. Then, we propose automated tools for analyzing log files and measuring the information gain from logs for different log analysis tasks such as anomaly detection. We then continue our NLP-enabled approach by leveraging the state-of-the-art language models, i.e., Transformers, to perform automated log parsing

Stepwise API usage assistance based on N-gram language models

Software development requires the use of external Application Programming Interfaces (APIs) in order to reuse libraries and frameworks. Programmers often struggle with unfamiliar APIs due to their lack of resources or less common design. Such difficulties often lead to an incorrect sequences of API calls that may not produce the desired outcome. Language models have shown the ability to capture regularities in text as well as in code. In this work we explore the use of n-gram language models and their ability to capture regularities in API usage through an intrinsic and extrinsic evaluation of these models on some of the most widely used APIs for the Java programming language. To achieve this, several language models were trained over a source code corpora containing several hundreds of GitHub Java projects that use the desired APIs. In order to fully assess the performance of the language models, we have selected APIs from multiple domains and vocabulary sizes. This work allowed us to conclude that n-gram language models are able to capture the API usage patterns due to their low perplexity values and their high overall coverage, going up to 100% in some cases, which encouraged us to create a code completion tool to help programmers stay in the right path when using unknown APIs while allowing for some exploration.O desenvolvimento de software requer a utilização de Application Programming Interfaces (APIs) externas com o objectivo de reutilizar bibliotecas e frameworks. Muitas vezes, os programadores têm dificuldade em utilizar APIs desconhecidas, devido à falta de recursos ou desenho fora do comum. Essas dificuldades provocam inúmeras vezes sequências incorrectas de chamadas às APIs que poderão não produzir o resultado desejado. Os modelos de língua mostraram-se capazes de capturar regularidades em texto, bem como em código. Neste trabalho é explorada a utilização de modelos de língua de n-gramas e a sua capacidade de capturar regularidades na utilização de APIs, através de uma avaliação intrínseca e extrínseca destes modelos em algumas das APIs mais utilizadas na linguagem de programação Java. Para alcançar este objectivo, vários modelos foram treinados sobre repositórios de código do GitHub, contendo centenas de projectos Java que utilizam estas APIs. Com o objectivo de ter uma avaliação completa do desempenho dos modelos de língua, foram seleccionadas APIs de múltiplos domínios e tamanhos de vocabulário. Este trabalho permite concluir que os modelos de língua de n-gramas são capazes de capturar padrões de utilização de APIs devido aos seus baixos valores de perplexidade e a sua alta cobertura, chegando a atingir 100% em alguns casos, o que levou à criação de uma ferramenta de code completion para guiar os programadores na utilização de uma API desconhecida, mas mantendo a possibilidade de a explorar

A Learning Approach for Local Expert Discovery

Local experts are critical for many location-sensitive information needs, and yet there is a research gap in our understanding of the factors impacting who is recognized as a local expert and in methods for discovering local experts. Hence, this thesis: (i) proposes a geo-spatial learning-based framework, Local Expert Learning (LExL), for integrating multidimensional factors impacting local expertise, e.g. user-based, list-based, location-based and content-based features; (ii) accomplishes a comprehensive controlled study over AMT-labeled local experts on eight topics and in four cities, which not only leverages the candidates’ basic information, but also considers the location authority impacting a candidate’s expertise; and (iii) develops a prototype system, Local Experts Visualizing and Rating System (LEVRS), for visualizing and rating local experts. We find significant improvements (around 45% in precision and 50% in NDCG) of finding local experts compared to two state-of-the-art alternatives as well as evidence of the generalizability of the learned local expert ranking models to new topics and new locations

Invisible Search and Online Search Engines

" Invisible Search and Online Search Engines considers the use of search engines in contemporary everyday life and the challenges this poses for media and information literacy. Looking for mediated information is mostly done online and arbitrated by the various tools and devices that people carry with them on a daily basis. Because of this, search engines have a significant impact on the structure of our lives, and personal and public memories. Haider and Sundin consider what this means for society, whilst also uniting research on information retrieval with research on how people actually look for and encounter information. Search engines are now one of society’s key infrastructures for knowing and becoming informed. While their use is dispersed across myriads of social practices, where they have acquired close to naturalised positions, they are commercially and technically centralised. Arguing that search, searching, and search engines have become so widely used that we have stopped noticing them, Haider and Sundin consider what it means to be so reliant on this all-encompassing and increasingly invisible information infrastructure. Invisible Search and Online Search Engines is the first book to approach search and search engines from a perspective that combines insights from the technical expertise of information science research with a social science and humanities approach. As such, the book should be essential reading for academics, researchers, and students working on and studying information science, library and information science (LIS), media studies, journalism, digital cultures, and educational sciences.

Journey to the centre of a news black hole: examining the democratic deficit in a town with no newspaper

Circulation and revenue declines affecting the newspaper industry are causing changes in the way local newspapers are run. Journalism has been withdrawing from communities and some local newspapers have closed. The resulting gap in local news and information has been called a news black hole. This research takes one such news black hole – Port Talbot – and examines it longitudinally from the point of view of: 1) the quantity and quality of news in the 39 years before and the four years after the 2009 newspaper closure; 2) changes in newsgathering and journalism practices; 3) the community’s ability to access the information, representation and scrutiny normally associated with fourth estate journalism; and 4) the community’s civic and democratic behaviour before and after the closure. It builds on Habermas’s theory of the public sphere, theorising the existence of local public geo-spheres, and that damage to these at the local level may entail damage to the whole public sphere. This multi-method study finds that the quantity of local news halved after the closure of the newspaper, and that its quality declined from the 1990s onwards. Although the loss of the newspaper was important, so was the gradual withdrawal of journalism from the town, marked by steep declines in journalist numbers and the closure of district newspaper offices. It also finds newsgathering has become more distant from communities and is more likely to use press releases and high status or official sources, and less local and less likely to be witnessed by a journalist. It finds the community under-informed, under-represented, and unable to access timely local information or gain adequate access to scrutiny. The democratic measure of election turnout in particular declined from around the time the district offices closed. Together, these findings suggest damage to the local public sphere in the town