Museums, Climate Change and Energy Education: A Digital Discourse Analysis
Bibliographic record
Abstract
Abstract The purpose of this study was to explore how science and environmentally related museums in Alberta, Canada are digitally engaging with climate change and energy education. This inquiry utilised qualitative discourse analysis to examine the discourses, dynamics and tensions present in digital museum contexts related to climate and energy education in Alberta. Drawing on Eisner’s three curricula — the explicit, implicit and null — the study focused on museums’ websites and social media activity. The museums studied share common foci on science, environment, or energy but range in size and location. As a long-standing energy-based economy, Alberta provides an interesting, and often contested, setting to observe climate and energy education in practice at museums, many of which exist in communities and within governance and stakeholder networks which are connected to the energy industry. Discourse-connected findings, discussion and implications are presented in relation to museums’ institutional mandates, curricular initiatives, pedagogical practices, special events and infrastructure initiatives.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".