Citizen science evolved through multiple disciplinary manifestations into a new field of study and a participatory method of enquiry. While most citizen science projects take place within problem-focused natural sciences, social sciences and humanities help understanding the human dimension and open a broad methodological spectrum for enriching scientific research with new approaches and for boosting public participation. In this paper, we use a meta-synthesis approach to explore how citizen science is practised in the so far less addressed social sciences and humanities by focusing on the role of the citizens, the goals and approaches of the projects, the tasks in which citizens are engaged and their gains across projects of diverse disciplinary background. Our findings indicate that social sciences are gaining more acknowledgment within interdisciplinary citizen science projects by addressing ‘wicked’ problems of human behaviour and agency, while humanities are in quest of a better-defined locus in citizen science. We conclude that social sciences and humanities still face considerable barriers to infiltrate citizen science; the payoffs are substantial and already rewarding for several subfields in social sciences and humanities.

Citizen humanities is the term for citizen ‘science’ in the humanities. It has a long tradition and, since the object of investigation is human culture, raises questions about values, cultural significance, and deeper meaning of phenomena related to human culture.The development of digital technologies not only led to the emergence of digital humanities but also to new ways of involving citizens in the activities of cultural heritage institutions and academic research. Participants’ contributions to academic research and to the preservation of cultural heritage range from uncovering treasures hidden in archives and digital environments to tapping local knowledge. Their tasks have included tagging, transcribing, or cataloguing artefacts, through which they acquire specialist knowledge and competences, while assisting scholars and researchers to gain new insights. Challenges in the citizen humanities include biases, participant training and retention, as well as the advancement of digital technologies, such as artificial intelligence.Citizen humanities can combine topical issues in society with academic knowledge, demonstrate the relevance of the humanities for society, and establish a direct link to its members. In addition to the advancement of knowledge, the citizen humanities can unlock the potential of embedded, diverse, and culturally sensitive knowledge and play a crucial role in preserving and enriching cultural heritage.

Christian Lang, Lennart Wachowiak, Barbara Heinisch, Dagmar Gromann. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021.

Translation plays an important role in all areas of human activity. Despite its primary role of overcoming language barriers, it is used as an analogy for activities that require transfer, mediation, or negotiation of meaning. Knowledge translation is a concept that links knowledge to action, which is also at the heart of citizen science. Several studies have highlighted the ways in which citizen science can contribute to the definition, monitoring and implementation of the United Nations’ Sustainable Development Goals (SDGs). Although these studies emphasized the importance of data contributions for SDG reporting and monitoring purposes, this paper applies the concept of knowledge translation to citizen science for achieving the SDGs based on the conceptual framework provided by translation studies. Knowledge translation, citizen science, and the SDGs have their focus on actions and negotiations in common. Citizen science can, thus, be regarded as a mediator between science and the SDGs or a mediator between the public and policymakers. Exemplified by biocultural diversity, this paper analyzes the application of knowledge translation to the SDGs in and through citizen science. Citizen science guided by the SDGs requires different forms of knowledge ((and) translation) that are usable, accessible, and meaningful.

Citizen science has become a world-wide phenomenon. Especially for citizen science projects that have a global reach, translation is crucial to overcome language and cultural barriers to reach members of the public. Translation, understood as the transfer of meaning (of a text) from one language into another language, is crucial for the transmission of information, knowledge and (social) innovations. Therefore, this paper examines the role of translation and terminology used in citizen science projects and how translation can foster (or impede) social innovation through citizen science activities. Based on a set of predefined criteria derived from the social innovation literature, this paper analyzes the factors that contribute to (social) innovation in citizen science by means of translation. A specific focus of the case study is on the aspects of agency, institutions, and social systems. The results demonstrate that translation in citizen science may support a change of social practices as ingredients of social innovations. Additional research is needed to further understand the implications of translation in citizen science and its effects on social innovation. Nevertheless, this work has been one of the first attempts to examine the relation between translation, citizen science and social innovation.

Text-generative large language models (LLMs) offer promising possibilities for terminology work, including term extraction, definition creation and assessment of concept relations. This study examines the performance of ChatGPT, Perplexity and Microsoft CoPilot for conducting terminology work in the field of the Austrian and British higher education systems using strategic prompting frameworks. Despite efforts to refine prompts by specifying language variety and system context, the LLM outputs failed to reliably differentiate between the Austrian and German systems and fabricated terms. Factors such as the distribution of German-language training data,potential pivot translation via English and the lack of transparency in LLM training further complicated evaluation. Additionally, output variability across identical prompts highlights the unpredictability of LLM-generated terminology. The study underscores the importance of human expertise in evaluating LLM outputs, as inconsistencies may undermine the reliability of terminology derived from such models. Without domain-specific knowledge (encompassing both subject-matter expertise and familiarity with terminology principles) as well as LLM literacy, users are unable to critically assess the quality of LLM outputs in terminological contexts. Rather than indiscriminately applying LLMs to all aspects of terminology work, it is crucial to assess their suitability for specific tasks.