The Methodological Unit organizes a weekly Brown Bag Seminar to highlight novel methodological approaches in humanities and social sciences. The idea of the meetings is to introduce methodological innovations and cutting-edge research in various disciplines in an easily accessible manner and have an interdisciplinary discussion in an easy-going atmosphere over lunch. Bring your own lunch, we bring fresh methodological topics!
Every Wednesday at 12.15.
Exceptions: On Wednesday 28.1. there is no Brown Bag Seminar, instead we invite everyone to join AI for SSH Research network's kick off event on Thursday 29.1. More info and sign up below.
You are welcome to join us at seminar room 524, Fabianinkatu 24 A, 5th floor, or online via Zoom.
There will be a 20-minute introduction to the methodological theme, followed by an open discussion of 40 minutes. The seminars are open to everybody. We expect a multidisciplinary and methodologically curious audience from different faculties and units of the central campus. The language of the meetings can be Finnish or English.
The most important prerequisite for participation is not methodological expertise, but an open mind towards new methodological innovations and discussion across methodological and disciplinary boundaries.
Scroll down for the upcoming program of Brown Bag Seminars. To get notified on updates sign up for our mailing list or follow us on social media.
Please note! This is a special event in place of the regular Brown Bag Seminar. Sign up is required and the location and time are different. Details can be found below.
You are warmly invited to the kick-off event of the AI for SSH Research network on 29 January 2026. The initiative is led by the AI researchers and leadership of the HSSH institute and the DIVSOL profiling area. Its aim is to establish a collaborative network to advance the use of AI for research in social sciences and humanities and to foster the exchange of ideas and experiences on the topic.
The meeting will take place on Thursday, 29 January 2026, from 13:00 to 17:00 in Kaisa Hall (Kaisa-sali), located on the seventh floor of the Kaisa Library (Fabianinkatu 30).
Talks will be given by Arto Klami, Sofoklis Kakouros, Matti Pohjonen, Eetu Mäkelä, and Anton Berg. The programme will also include workshop sessions dedicated to planning the network’s activities, as well as a coffee break for informal discussions.
A more detailed programme will be sent to registered participants after the registration deadline.
We kindly ask you to register on 20 January at the latest here:
A detailed programme will be sent to registered participants closer to the event.
Looking forward to seeing you on 29 January.
A number of recent papers have used unsupervised image clustering methods for social science questions. However, there has not yet been research examining whether the clusters resemble the kinds of groupings that social scientists identify when conducting traditional image analysis. In response, we compare the clusters emerging through previously recommended unsupervised image clustering methods in social sciences with the manually categorised groupings to determine which, if any, of these methods are able to imitate the ‘social scientist’s gaze’. We use four different conceptual lenses from previous work, theme, content, purpose and type and use various metrics to assess the similarities. We show that none of the methods can consistently match manual analysis. Therefore, while unsupervised methods for image clustering can identify visually similar images, this may not be sufficient to capture the full breadth of methodological and theoretical approaches common for social sciences. Our work suggests that social scientists should consider unsupervised approaches as a distinct gaze or focus on developing methods which align with previous social scientists’ gazes.
Adeline Clarke is a doctoral researcher in the Social Computing Group at the University of Helsinki where she is researching multimodal large language models for use in qualitative content analysis in the social sciences. Prior to living in Finland, Adeline worked as a data scientist within industry and the Australian public service.