Toward an Affective Turn: Hosting a Mental Health Exhibition at a Science Centre
Bibliographic record
Abstract
Mental health issues are affecting individuals worldwide. It has been argued that science museums can play an important role in helping to confront social stigmas by joining conversations and enacting practices about mental health. In this paper, we focus on the exhibition Mental Health: Mind Matters developed originally in Finland, by Heureka The Finnish Science Centre, and then premiered at the Science Museum of Minnesota (SMM; St Paul, U.S.A.) in 2018. Through a qualitative case study research approach, we asked: (1) What tensions and opportunities emerged as museum staff prepared the Finnish exhibit for its premier at the SMM?, and (2) How did the experience of working with a mental health exhibition speak to broader social roles and purposes of science museums? Data was collected on site and included: interviews with museum professionals, institutional documents, photographic records of the exhibition, and field notes written independently by both co-authors and research assistants. The findings of this study allowed us to identify events or moments of productive struggle experienced by museum professionals while preparing the exhibition for public consumption in the U.S.A. They also revealed important aspects related to a movement towards an affective turn in science museums marked by institutional values and practices that embrace compassion, humility, self-reflection, and the need for safe spaces for dialogue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".