Browsing by study line "Social Data Science"

The EU has been in the recent decades one of the most important innovators globally. However, due to the recent events in its external environment, such as supply chain disruptions caused by COVID-19, third-country dependencies and external geopolitical pressures, its position in global innovation is challenged. These developments are often discussed under the headline “strategic autonomy”, which refers to the EU’s capacity to act autonomously in key strategic policy areas. As capacity to innovate is what crucially undergirds the EU’s economic power, the EU’s innovation policy plays a fundamental role in achieving strategic autonomy. Yet, peer reviewed study of the implications of strategic autonomy for EU innovation policy has remained thus far uncharted. This thesis asks: how the discourse on strategic autonomy frames EU innovation policy? To answer the research question, this thesis deployed a framing analysis in parallel with qualitative content analysis. This approach was applied to policy documents published by the European Commission in the period of 2019-2023. This thesis aimed to investigate the extent to which strategic autonomy has influenced EU innovation policy. Secondly, the thesis explored how the concept(s) inform policymakers about the identified problem and examines the policy justifications that arise from this assessment. The qualitative content analysis found that the discourse on strategic autonomy has become increasingly relevant for the EU innovation policy, showcasing high levels of frequency in the use of the concept in the policy area. Moreover, a closer look at the use of the concept revealed that not only is the concept invoked frequently, but has only become highly influential for the shaping of the policy. In addition, an overarching framing could be identified that perceives the environment external to the EU being increasingly hostile and competitive, complicated by antagonisms towards the rules-based world order. From this assessment arises novel policy justifications for international cooperation in R&I. Also, EU innovation policy is increasingly interested in securing autonomous capacity to innovate and have access and control of critical technologies. In terms of international cooperation, a balancing act between openness and assertiveness could be observed. The analysis also had implications for internal developments as the EU is showing a much more active role in directing funding to critical sectors to enhance its industrial capacity and competitiveness.

The self-organizing map (SOM) is a form of unsupervised neural network and a method for data analysis that allows reducing the dimensionality of data, exploring the variation and dependencies between variables and presenting their similarity relations. Being a powerful visualization instrument and having a strong disposition for clustering, the self-organizing map could be implemented to the analysis of survey data, particularly, collected with the questionnaires. This thesis provides a relevant example of dealing with the limited size mixed survey data set. The self-organizing map algorithm is implemented to analyze the data obtained from the faculty well-being project organized at the Faculty of Social Sciences in the University of Helsinki. The set of experiments utilize the self-organizing map algorithm to explore a possible clustering structure of the data and identify the profiles of the survey participants. Each of three experiments illustrates different variable encoding approaches for the sets of closed background and Likert scale questions. The largest number of the profiles was obtained from the final experiment. Four out of seven profiles represent clusters of the individuals with mainly neutral, negative or very negative experiences related to the well-being at the faculty. The data analysis experiments also illustrate the possible challenges of the SOM method implementation to survey data. The existence of categorical variables, the necessity of choosing a set of parameters for the SOM training and dealing with the missing values are discussed as main challenges of the SOM implementation to survey data analysis using the R package “kohonen”.