Exploring the Educational Potential of Climate Change Exhibitions in Natural History Museums
Bibliographic record
Abstract
Abstract Climate change (CC) is a critical socio-scientific issue that requires special attention in biology education. Natural History Museums (NHMs) offer an authentic learning environment where CC can be presented, thus providing unique opportunities for instruction. This qualitative study examined the educational potential of two CC exhibitions at NHMs. Interviews were conducted with museum staff to learn about the educational goals they associate with the exhibitions. Four key educational goals emerged from the findings: (1) Grounding knowledge in science, (2) Grounding knowledge in Nature of Science (NOS), (3) Visitors’ emotional connections to CC, and (4) Encouraging personal activism. These goals all contribute to a deeper appreciation of the scientific process. In addition to the interviews, we analyzed the content of both CC exhibitions to identify which aspects of NOS were integrated. The findings showed that the exhibitions differed in terms of the emphasis on the cognitive-epistemic vs. the institutional-social aspects of NOS. Despite these differences, both exhibitions sought to enhance visitors' understanding of the complexity of CC while promoting informed discourse, personal engagement, and activism. The discussion centers on ways biology educators can leverage NHM exhibitions to impart scientific knowledge on CC while addressing related topics in the classroom.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".