Inclusive Democracy, Sustainable Democracy?

PhD Thesis by Katharina Holec

In my PhD thesis at the University of Düsseldorf I look at the effects of decriptive and substantive representation in consultative citizen participation on legitimacy beliefs of individuals.


Legitimacy – as a sum of individual beliefs about the appropriateness and acceptability of a political community, its regime and authorities – is the key element in stabilizing nowadays democratic systems. But, dissatisfaction with the performance of political systems is increasing and understandings of democracy can be divergent. Especially when political involvement is reduced to the possibility of choosing representatives legitimacy beliefs remain hard to rebuilt and understandings of democracy remain hard to align between different citizens. To solve this “legitimacy problem” plenty democratic theorists and researchers suggest more possibilities for political participation in the democratic process. Consultation is one mean often used by local municipalities to increase satisfaction and understanding of political processes. But consultative participation often promises too much. Like all political participation consultation is biased. Social inequality in society influences who participates. And who participates will ultimately influence a processes outcome. . The risk of losing marginalized voices in the process is high.

I want to enable a detailed understanding of the advantages of including these voices for local democratic legitimacy beliefs. Therefore, I follow Pitkin’s (1972) ideas on descriptive and substantive representation applying them to a consultative participation process. I ask

(a) Does descriptive representation in the input of a consultative participation process increase substantive representation in the throughput and outcome of a political process?

(b) How important are descriptive and substantive representation for increasing legitimacy beliefs after the political process?

I focus specifically on three levels of the policy making process (1) the input level, where I consider descriptive representation to be relevant, (2) the throughput level, where I consider substantive representation as ‘speaking for’ relevant and (3) the outcome level, where I consider substantive representation as ‘acting for’ by local municipalities relevant. While I consider (1) and (2) to be relevant criteria for increasing legitimacy beliefs by improving the political process, I consider (3) to be relevant for increasing legitimacy beliefs by improving real life living conditions.

Mobility Transition through Participation? Policy impact of discursive, consultative public participation on urban transport projects for sustainability

Dissertation Projekt, Laura Mark

In my dissertation project at the Faculty of Architecture at RWTH Aachen University, I am using two case studies to investigate the substantive impact of consultative public participation on political decisions and the implications for sustainable development. My object of investigation is planning for the sustainable mobility transition, since on the one hand it is important and urgent for sustainable development and on the other hand it directly affects people’s everyday lives and thus often leads to resistance.


A socio-ecological shift in transport requires profound changes in public space that affect the daily lives of users. This redistribution of road space and change in conditions of use is primarily carried out through spatial planning on the part of the public sector, in which the public is also increasingly involved. This is usually associated (implicitly or explicitly) with the public having an influence on the content of the planning; however, the actual effect has hardly been researched.

I am investigating the mechanisms through which the substantive impact of public participation comes about or is prevented, and which factors influence these mechanisms. I am interested in the conditions under which these substantive effects contribute to integrated transport planning, measured both in terms of democratic theory and substantive criteria.

Two municipal transport transition projects in Hamburg serve as case studies, in which the public can participate or has participated through consultation offers and other forms of participation: the redesign of the Elbchaussee in Hamburg and the low-car design of the Ottensen neighbourhood in Hamburg. The processes differ, among other things, in their framework conditions, spatial scale, tasks and participation offerings. For the detailed reconstruction and analysis of these processes, I mainly rely on data from qualitative interviews, document and media analyses, supplemented by results of quantitative population and participant surveys.

Expected Results

Expected results are theses on public participation in the context of the mobility transition. These deal with the mechanisms and factors that influence policy impact and come about through a detailed analysis of the individual case studies, a targeted comparison of the two case studies with each other and the embedding of the empirical results in the state of research as well as other results from the project. These theses are intended to contribute to the discussion on the role of the public in the context of a socio-ecological transformation.

Supporting the Manual Evaluation Process of Citizen’s Contributions Through Natural Language Processing

Doctoral thesis (full text) of Julia Romberg

Engaging citizens in decision-making processes is a widely implemented instrument in democracies. On the one hand, such public participation processes serve the goal of achieving a more informed process and thus to potentially improve the process outcome, i.e. resulting policies, through the ideas and suggestions of the citizens. On the other hand, involving the citizenry is an attempt to increase the public acceptance of decisions made.

As public officials try to evaluate the often large quantities of citizen input, they regularly face challenges due to restricted resources (e.g. lack of personnel,time limitations). When it comes to textual contributions, natural language processing (NLP) offers the opportunity to provide automated support for the evaluation, which to date is still carried out mainly manually. Although some research has already been conducted in this area, important questions have so far been insufficiently addressed or have remained completely unanswered.

My dissertation, which I successfully completed in 2023, therefore focused on how existing research gaps can be overcome with the help of text classification methods. A particular emphasis was placed on the sub-tasks of thematic structuring and argument analysis of public participation data.

The thesis begins with a systematic literature review of previous approaches to the machine-assisted evaluation of textual contributions (for more insights, please refer to this article). Given the identified shortage of language resources, subsequently the newly created multidimensionally annotated CIMT corpus to facilitate the development of text classification models for German-language public participation is presented (for more insights, please refer to this article).

The first focus is on the thematic structuring of public input, particularly considering the uniqueness of many public participation processes in
terms of content and context. To make customized models for automation worthwhile, we leverage the concept of active learning to reduce manual workload by optimizing training data selection. In a comparison across three participation processes, we show that transformer-based active learning can significantly reduce manual classification efforts for process sizes starting at a few hundred contributions while maintaining high accuracy and affordable runtimes (for more insights, please refer to this article). We then turn to the criteria of practical applicability that conventional evaluation does not encompass. By proposing measures that reflect class-related demands users place on data acquisition, we provide insights into the behavior of different active learning strategies on class-imbalanced datasets, which is a common characteristic in collections of public input.

Afterward, we shift the focus to the analysis of citizens’ reasoning. Our first contribution lies in the development of a robust model for the detection of argumentative structures across different processes of public participation. Our approach improves upon previous techniques in the application domain for the recognition of argumentative sentences and, in particular, their classification as argument components (for more insights, please refer to this article). Following that, we explore the machine prediction of argument concreteness. In this context, the subjective nature of argumentation was accounted for by presenting a first approach to model different perspectives in the input representation of machine learning in argumentation mining (for more insights, please refer to this article).