Category: research areas

Consultation of citizens in mobility projects: The participation landscape at municipal level in Germany

In this presentation at the 18th annual conference of the Mobility and Transport Working Group (AK MoVe) 2023, Laura Mark, Katharina Holec and Tobias Escher presented a survey on the scope and design of consultation in municipal mobility planning. From the results, statements can be derived about the participation landscape in Germany.

Abstract

Municipalities as key actors in the transport transition are increasingly using consultative public participation in planning. So far, however, it is unclear to what extent they use participatory processes in mobility-related planning and how these are designed. Given the challenges associated with the transition to a climate-neutral transport system, taking stock of existing efforts is highly relevant in order to assess the practical significance of participation processes and to better investigate the role of different types of procedures and contexts.

This study fills this gap based on an analysis of the consultative, discursive participation processes for mobility-related planning in German cities since 2015. The study examined ‘participation-oriented’ cities with guidelines for citizen participation, which were compared to a random selection of ‘typical’ municipalities in North Rhine-Westphalia, Baden-Württemberg and Saxony as well as the three German city states.

Based on these approximately 180 cities and 350 procedures, it becomes clear that discursive consultations are carried out regularly, in particular in municipalities with guidelines and larger cities. Worth criticizing is that the formats used can usually reach only certain groups of the population and that for a significant proportion of the processes examined no information on the results of participation can be found. This means that the potentials of discursive citizen participation in addressing the municipal transport transition have not yet been sufficiently utilised.

Key Findings

  • Participation in municipal planning procedures related to mobility is no longer an exception, but not yet the rule either. Based on the data of our sample, it can be presumed that in most municipalities in Germany there was no possibility to participate in such procedures in the period under consideration.
  • In general, cities with guidelines involved their citizens more frequently, more often and with more diverse topics and formats. Medium-sized and large cities consulted their citizens significantly more often than small towns.
  • Weaknesses are evident in the participation formats used: The majority of municipalities relied on self-selected selection processes. First attempts with target group-specific formats or random selection can be found mainly in the municipalities with guidelines and in the city states. A large proportion of the procedures were also carried out purely online.
  • For 5 to 10% of the procedures, no current status could be found, and for a larger proportion it was unclear what happened after the consultation. This is true for all municipalities, although less so for those with guidelines, and can be regarded as a lack of transparency and impact of participation.

Publication

Mark, Laura; Holec, Katharina; Escher, Tobias (2024): Die Beteiligung von Bürgerinnen und Bürgern bei kommunalen Mobilitätsprojekten: Eine quantitative Erhebung konsultativer Beteiligungsverfahren in Deutschland. In: RuR (Spatial Research and Planning). DOI: 10.14512/rur.2239.

MA-thesis on participation of pupils during the Corona pandemic

In her thesis for the MA Social Sciences: Social Structures and Democratic Governance at Heinrich-Heine-University Düsseldorf, Maria Antonia Dausner has investigated the possibilities of pupil participation during the Covid-19-related school closures, focusing on an analysis of selected elementary schools in North Rhine-Westphalia.

More information is available in German.

The Structure and Antecedents of Citizens’ Perceptions of Local Democracy: Findings from a Survey in Different German Cities in 2021

Abstract

Legitimacy is the voluntary recognition of political authority, which plays an important role in the stability and governance of political systems. At the system level, it is strongly conditioned by individual legitimacy attitudes at the micro level. The goal of our presentation is to illustrate and understand

  • How different objects of political support are constructed and interrelated (trust, satisfaction, and legitimacy beliefs)?
  • How strongly local and national political attitudes toward objects influence each other?
  • What individual factors ultimately influence local and national legitimacy beliefs?

To measure these relationships, we used survey data collected in the project to first operationalize the constructs of satisfaction with authority, trust in institutions, and legitimacy attitudes at the local and national levels. Methodologically, we use a confirmatory factor analysis and OLS regression.

Key Findings

  • Higher satisfaction with local than with national authorities, and greater trust in local than in national institutions, while mean differences in legitimacy attitudes vary
  • Strong correlations between the concepts of trust and satisfaction and legitimacy beliefs
  • Strong correlations between local and national levels for trust, satisfaction, and legitimacy beliefs
  • Hardly any systematic influences by individual factors on legitimacy beliefs when controlling for satisfaction and trust as influences on legitimacy

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.

Summary

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.

Overview of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public Participation

In this publication in Digital Government: Research and Practice Julia Romberg and Tobias Escher offer a review of the computational techniques that have been used in order to support the evaluation of contributions in public participation processes. Based on a systematic literature review, they assess their performance and offer future research directions.

Abstract

Public sector institutions that consult citizens to inform decision-making face the challenge of evaluating the contributions made by citizens. This evaluation has important democratic implications but at the same time, consumes substantial human resources. However, until now the use of artificial intelligence such as computer-supported text analysis has remained an under-studied solution to this problem. We identify three generic tasks in the evaluation process that could benefit from natural language processing (NLP). Based on a systematic literature search in two databases on computational linguistics and digital government, we provide a detailed review of existing methods and their performance. While some promising approaches exist, for instance to group data thematically and to detect arguments and opinions, we show that there remain important challenges before these could offer any reliable support in practice. These include the quality of results, the applicability to non-English language corpora and making algorithmic models available to practitioners through software. We discuss a number of avenues that future research should pursue that can ultimately lead to solutions for practice. The most promising of these bring in the expertise of human evaluators, for example through active learning approaches or interactive topic modelling.

Key findings

  • There are a number of tasks in the evaluation processes that could be supported through Natural Language Processing (NLP). Broadly speaking, these are i) detecting (near) duplicates, ii) grouping of contributions by topic and iii) analyzing the individual contributions in depth. Most of the literature in this review focused on the automated recognition and analysis of arguments, one particular aspect of the task of in-depth analysis of contribution.
  • We provide a comprehensive overview of the datasets used as well as the algorithms employed and aim to assess their performance. Generally, despite promising results so far the significant advances of NLP techniques in recent years have barely been exploited in this domain.
  • A particular gap is that few applications exist that would enable practitioners to easily apply NLP to their data and reap the benefits of these methods.
  • The manual labelling efforts required for training machine learning models risk any efficiency gains from automation.
  • We suggest a number of fruitful future research avenues, many of which draw upon the expertise of humans, for example through active learning or interactive topic modelling.

Publication

Romberg, Julia; Escher, Tobias (2023): Making Sense of Citizens’ Input through Artificial Intelligence. In: Digital Government: Research and Practice, Artikel 3603254. DOI: 10.1145/3603254.

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.

Abstract

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).

Expert evidence: State of research on opportunities, challenges and limitations of digital participation

As set out in the German Site Selection Act (StandAG), the Federal Office for the Safety of Nuclear Waste Management (BASE) is charged with the comprehensive information and participation of the public in regards procedure for the search and selection of a repository site for the final disposal of high-level radioactive waste. In this context, in February 2022 BASE commissioned an expert report on the “Possibilities and limits of digital participation tools for public participation in the repository site selection procedure (DigiBeSt)” from the Düsseldorf Institute for Internet and Democracy (DIID) at Heinrich Heine University Düsseldorf in cooperation with the nexus Institute Berlin. For this purpose, lead by Tobias Escher a review of the state of research and current developments (work package 2) was prepared has been summarised in a detailed report (in German).

Selected findings from the report are:

  • Social inequalities in digital participation are mainly based on the second-level digital divide, i.e. differences in the media- and content-related skills required for independent and constructive use of the internet for political participation.
  • Knowledge about the effectiveness of activation factors is still often incomplete and anecdotal, making it difficult for initiators to estimate the costs and benefits of individual measures.
  • Personal invitations have been proven to be suitable for (target group-specific) mobilisation, but the established mass media also continue to play an important role.
  • Broad and inclusive participation requires a combination of different digital and analogue participation formats.
  • Participation formats at the national level face particular challenges due to the complexity of the issues at stake and the size of the target group. Therefore, these require the implementation of cascaded procedures (interlocking formats of participation at different political levels) as well as the creation of new institutions.

Publication

Lütters, Stefanie; Escher, Tobias; Soßdorf, Anna; Gerl, Katharina; Haas, Claudia; Bosch, Claudia (2024): Möglichkeiten und Grenzen digitaler Beteiligungsinstrumente für die Beteiligung der Öffentlichkeit im Standortauswahlverfahren (DigiBeSt). Hg. v. Düsseldorfer Institut für Internet und Demokratie und nexus Institut. Bundesamt für die Sicherheit der nuklearen Entsorgung (BASE). Berlin (BASE-RESFOR 026/24). Available online https://www.base.bund.de/DE/themen/fa/sozio/projekte-ende/projekte-ende.html .

3rd workshop for practitioners on first results from surveys in case study municipalities

On 30 November we invited representatives of the municipalities with whom we cooperate in order to discuss the first results of the extensive surveys conducted by our research group. The focus was on the question of how the respective participation procedures are assessed by those participating and which aspects motivate or discourage such participation.

Despite the diversity of the five projects we examined (and the still small number of participants), the assessments of the people participating in such processes show a relatively high degree of agreement. Overall, the evaluations of the participation processes are rather positive with regard to the course of discussion and transparency. At the same time, however, there are also comparable challenges in all processes. For example, the representation of one’s own interests is rated as relatively good, but gaps in the representation of other opinions are perceived. Also, a balance of interests is not always achieved. Furthermore, the participants are rather sceptical about the actual impact of the participation results on the political process, even though they still deem such an impact possible.

There is more information available in German.

Enriching Machine Prediction with Subjectivity Using the Example of Argument Concreteness in Public Participation

In this publication in the Workshop on Argument Mining, Julia Romberg develops a method to incorporate human perspectivism in machine prediction. The method is tested on the task of argument concreteness in public participation contributions.

Abstract

Although argumentation can be highly subjective, the common practice with supervised machine learning is to construct and learn from an aggregated ground truth formed from individual judgments by majority voting, averaging, or adjudication. This approach leads to a neglect of individual, but potentially important perspectives and in many cases cannot do justice to the subjective character of the tasks. One solution to this shortcoming are multi-perspective approaches, which have received very little attention in the field of argument mining so far.

In this work we present PerspectifyMe, a method to incorporate perspectivism by enriching a task with subjectivity information from the data annotation process. We exemplify our approach with the use case of classifying argument concreteness, and provide first promising results for the recently published CIMT PartEval Argument Concreteness Corpus.

Key findings

  • Machine learning often assumes a single ground truth to learn from, but this does not hold for subjective tasks.
  • PerspectifyMe is a simple method to incorporate perspectivism in existing machine learning workflows by complementing an aggregated label with a subjectivity score.
  • An example of a subjective task is the classification of the concreteness of an argument (low, medium, high), a task whose solution can also benefit the machine-assisted evaluation of public participation processes.
  • First approaches to classifying the concreteness of arguments (aggregated label) show an accuracy of 0.80 and an F1 value of 0.67.
  • The subjectivity of concreteness perception (objective vs. subjective) can be predicted with an accuracy of 0.72 resp. an F1 value of 0.74.

Publication

Romberg, Julia (2022, October). Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity. In Proceedings of the 9th Workshop on Argument Mining (pp.115-125), Gyeongju, Republic of Korea. Association for Computational Linguistics. https://aclanthology.org/2022.argmining-1.11