2026-10-06 –, Room 1 Language: English
Introduction
Participatory approaches like citizen science can increase trust in science (e.g.; Bedessem et al., 2021, 2023; Wintterlin et al., 2022), not least by enhancing people’s science self-concept and science efficacy (PISA 2025 Science Framework, 2025). Certain characteristics of citizen science projects can make these benefits more or less likely to occur (Bonney et al., 2016; Moczek & Köhler, 2020), such as the role that involved citizens take on in the scientific process. Dependent on how citizen science practitioners conceptualise citizen science, citizens may be seen primarily as data providers (Bonney, 1996), or as collaborators in various aspects of scientific projects (Irwin, 1995). Theoretically, projects guided by the second conceptualisation are more likely to increase trust in science by enhancing preconditions of trust between career and citizen scientists (e.g., mutual respect, connectedness; Deci & Ryan, 2012). In light of intense normative and theoretical debate around the concept of citizen science (e.g.; Cooper et al., 2021), we aim to establish an empirical baseline of the status-quo of how citizen science and related approaches (i.e., community and participatory science, CCP) are currently conceptualised and implemented in the body of peer-reviewed literature. Specifically, we ask:
RQ1: How are the terms citizen science, community science and participatory science referred to, including their definitions, aims, and procedures, in the contemporary body of peer-reviewed literature?
RQ2: How are citizen science, community science, and participatory science practiced in terms of the degree of citizen/community/participatory scientist involvement?
RQ3: Based on a theoretical reflection of the findings, how can the different approaches to involving non-professional individuals in scientific research be expected to relate to participants’ self-referential perceptions of science (e.g., science self-concept, science self-efficacy, as defined in PISA 2025 Science Framework, 2025)?
Methods
We preregistered the study in line with the PRISMA protocol (Moher et al., 2015).
Search strategy
We searched SCOPUS, Web of Science Core Collection, and PubMed for the search terms “citizen scien”, “community scien” or “participatory scien*” in title, abstract, or keywords, published between 01.01.2021 and 13.11.2025. Detailed inclusion and exclusion criteria are outlined in Table 1. To ensure feasibility, we reduced the very large corpus (N = 9,006) by drawing a random sample of records (30%).
[TABLE 1]
Data analysis plan
Screening. An interdisciplinary team of 11 coders (from psychology, communication science, microbiology, and chemistry) will screen articles for eligibility. Articles are eligible if they report on a CCP project and are primary research articles (see Table 1 for more details). Coders will screen articles independently once interrater reliability was established on 5% of the respective corpora of abstracts/full texts.
Coding. For each eligible article, we will code (1) how authors conceptualise CCP by extracting text excerpts where authors refer to CCP (RQ1), and (2) what elements of the scientific process CCP scientists were involved in, ranging from research question development to results communication (RQ2; see Table 2 for more details on coding constructs; Heigl et al., 2020).
Data analysis. For RQ1, we will perform inductive content analysis of the text excerpts of conceptualisations of CCP science, to see how authors of peer-reviewed publications refer to these three terms. For RQ2, we will present descriptive statistics on which elements of the scientific process CCP scientists contributed to in the CCP projects.
Software. Both coding and content analysis will be supported by a Large Language Model (LLM) with strict human oversight and conservative model settings (Bermejo et al., 2025; Ziems et al., 2024). LLMs are well-suited for these tasks, as they have been shown to perform similarly well as human coders in deductive and inductive coding while operating in a time- and cost-effective manner (e.g.; Chew et al., 2023; Mathis et al., 2024).
Lastly, for RQ3, we link findings with relevant theory (Deci & Ryan, 2012) to infer implications of conceptualisations and involvement for CCP scientists’ science self-concept, science self-efficacy, enjoyment of science, and instrumental motivation—constructs considered important correlates of trust in science (PISA 2025 Science Framework, 2025).
[TABLE 2]
Outlook
We expect to complete eligibility screening by the end of August, and data extraction and analysis in September 2026. We thus expect to present initial results on the contents of CCP conceptualisations (RQ1) and CCP scientist involvement (RQ2), as well as potential psychological implications (RQ3). We will discuss the implications of our findings for citizen science and trust, as well as methodological choices, such as the chosen databases or the exclusion of grey literature.
I am an environmental psychologist by training, with a focus on understanding how perceptions, attitudes, and behaviours influence responses to major societal challenges, including climate change, public health threats, and science scepticism.