The Intersection Between Social-Institutional Aspects of Nature of Science and Social Justice in Natural History Museum Exhibitions
Bibliographic record
Abstract
Abstract Natural history museums (NHMs), once seen as elitist and colonial institutions, are now redefining their roles as agents for change and transformation in society. Many are committed to social justice, equity, and community engagement, which establishes them as significant cultural and educational entities. These museums often serve as hubs for scientific research and provide a uniquely authentic environment that promotes public engagement in scientific inquiry and exploration. Through their various initiatives, NHMs not only enhance the public’s understanding of scientific principles but also act as vital spaces for addressing broader societal issues, such as Social Justice (SJ). This study focused on the ways in which the intersection between Nature of Science (NOS) and SJ is presented in NHMs exhibitions, and in particular “The Changing Face of Science” series at The Field Museum in Chicago, USA. We collected data from museum signages and conducted a content analysis of four exhibitions presented in the museum. Our analysis was framed through the lens of NOS, and centered on the intersection of social-institutional aspects and SJ. The findings serve to develop a 7-category framework that characterizes this intersection and show how museum exhibitions can convey the relationships between science and societal issues. This framework contributes to the discourse on informal science education by demonstrating how NHMs integrate NOS and SJ, thereby promoting a more comprehensive and socially conscious understanding of science.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".