Representing biodiversity in science museums: perspectives from an STSE lens
Bibliographic record
Abstract
This paper explores how biodiversity is represented at the Life in Crisis Schad Gallery of Biodiversity (Royal Ontario Museum, Toronto). Using theoretical perspectives related to science, technology, society and environment (STSE), and biodiversity in science museums, we conducted a qualitative case study of the exhibit. Data collection included records of the exhibit through photos and videos; field notes; in-depth interviews with two museum professionals; and collection of documents and artifacts. The first analytical stage, based on inductive analysis, revealed that biodiversity is being represented as an ‘unknown’ topic, a loss and an unfinished narrative. The second analytical stage, deductively supported by the STSE framework, reflected a prominent logical reasoning angle, through which biological perspectives are routinary reflected in the exhibit. Our analyses also revealed hints of environmental ethics, stewardship, socio-ecojustice and value-centered views often associated in the exhibit with worldwide environmental issues and the idea of generic human beings. Our study suggests that the STSE lens can provide a framework to support the work of science museums around topics like biodiversity. It also highlights the importance of bringing local perspectives to the exhibit content, engaging with critical pedagogical strategies and amplifying the concept of sociobiodiversity.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.058 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".