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We describe an experience in teaching global software engineering (GSE) using distributed Scrum augmented with industrial best practices. Our unique instructional technique had students work in both same-site and cross-site teams to contrast the two modes of working. The course was a collaboration . Fifteen Canadian and eight Finnish students worked on a single large project, divided into four teams, working on interdependent user stories as negotiated with the industrial product owner located in Finland. Half way through the course, we changed the teams so each student worked in both a local and a distributed team. We studied student learning using a mixedmethod approach including 14 post-course interviews, pre-course and Sprint questionnaires, observations, meeting recordings, and repository data from git and Flowdock, the primary communication tool. Our results show no significant differences between working in distributed vs. non-distributed teams, suggesting that Scrum helps alleviate many GSE problems. Our post-course interviews and survey data allows us to explain this effect; we found that students over time learned to better self-select tasks with less inter-team dependencies, to communicate more, and to work better in teams.

Previous studies on intra-party competition have largely neglected the role played by geographic distance between co-partisan candidates. In this paper, we argue that candidates who live further away from intra-party competitors on the same party list benefit electorally from their remoteness. Moreover, we contend that the electoral effectiveness of exhibiting local personal vote attributesa theoretically and empirically well-established candidate strategy to cultivate personal votesalso depends on the geographical proximity of localized co-partisan candidates. Using a unique and untapped dataset of more than 6,000 Finnish election candidates' home address coordinates over four consecutive parliamentary elections, we run beta regression models to examine the effects of candidate remoteness and nearest candidates' local characteristics on intra-party vote shares. To measure the remoteness of a particular candidate, we develop a novel index based on the distribution of co-partisans over concentric circles around that candidate. The beta regression models and a battery of robustness tests confirm that geographic remoteness is associated with higher intra-party vote shares, and that nearby located localized candidates prove electorally harmful. Our findings have important implications for politicians' careers, party nomination strategies and future empirical research on intra-party competition.

It is often claimed that computational methods for examining textual data give good enough party position estimates at a fraction of the costs of many non-computational methods. However, the conclusive testing of these claims is still far from fully accomplished. We compare the performance of two computational methods, Wordscores and Wordfish, and four non-computational methods in estimating the political positions of parties in two dimensions, a left-right dimension and a progressive-conservative dimension. Our data comprise electoral party manifestos written in Finnish and published in Finland. The non-computational estimates are composed of the Chapel Hill Expert Survey estimates, the Manifesto Project estimates, estimates deriving from survey-based data on voter perceptions of party positions, and estimates derived from electoral candidates’ replies to voting advice application questions. Unlike Wordfish, Wordscores generates relatively well-performing estimates for many of the party positions, but despite this does not offer an even match to the non-computational methods.

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